Now imagine reviewing your underwriting guidelines and portfolio strategy with the same frequency. Welcome to the current state of risk policy hygiene at most credit unions.

In a world where consumer behavior changes faster than your ALCO committee can react, this is a recipe for missed signals, elevated delinquency, and outdated pricing models. So, what do the big banks do that credit unions should be doing?

They operate like agile development teams.

What Banks Know That You Might Not

Large financial institutions run portfolio risk like a DevOps team—constantly monitoring, testing, and deploying new risk models in near real-time. JPMorgan Chase’s annual risk report reveals they use ML-driven tools to test thousands of policy permutations and deploy updates quarterly—if not monthly.

Capital One, Citi, Wells Fargo? Same playbook. They’re not reacting to behavior shifts—they’re predicting them. And course-correcting before portfolio performance degrades.

Credit unions, on the other hand, tend to treat lending strategy like it’s carved in stone. Quarterly reviews. Manual analysis. Rearview mirror management.

It’s time to bring in a toothbrush for your underwriting policy. And that toothbrush is called dotData.

Agile Underwriting: The MVP You Never Knew You Needed

Let’s apply Agile principles to lending.

• Sprint Planning = Monthly Portfolio Review
  Instead of annual policy overhauls, why not sprint through one risk dimension at a time? One month it’s DTI segmentation. The next, application channel or payment behavior. Move fast. Iterate faster.
• Standups = Model Monitoring
  dotData’s platform makes it possible to auto-monitor your models for drift, performance degradation, and even identify new risk signals—daily.
• Demo Day = Underwriting Committee
  Your next meeting isn’t about guessing what’s behind your rising 90-day delinquency rate—it’s about showcasing how dotData detected a behavioral shift in first-time borrowers, built a better model, and shipped it. In a week.

dotData: Your Model Hygiene Coach

dotData is purpose-built for institutions between $500M and $5B in assets with active auto lending programs and real pain around model blind spots, LOS performance, and analytic bottlenecks.

Here’s what it delivers:

• 50x Faster Feature Engineering
  That means you get better models without waiting on manual data prep. Your analysts can focus on strategy instead of SQL queries.
• LOS Model Auditing
  Still relying on Open Lending, Zest.ai, or another black-box vendor? dotData helps you independently validate those models and see where they’re underperforming or mispricing risk.
• Guideline Optimization
  Think: intelligent rate cards, dynamic tiered pricing, and predictive approval matrices. No more “gut feeling” underwriting.
• Behavioral Signal Discovery
  Uncover emerging patterns like gig workers showing stronger repayment than salaried peers, or how TikTok-finfluenced borrowers use credit differently. Yes, it finds those.

Why This Matters More Than Ever

We’re entering an era of behavioral unpredictability, economic volatility, and changing borrower expectations. The pandemic rewired how consumers borrow, spend, and pay. AI-fueled fintechs are iterating faster than ever. And inflationary shocks are causing shifts in everything from savings rates to early payoff behavior.

The worst thing you can do is assume last year’s policies still serve you.

dotData gives you the agility of a big bank—without hiring a team of 20 data scientists.

Final Word: Risk Management Is Hygiene

Here’s the blunt truth: if you’re not constantly scanning and adjusting your lending policies, you’re the financial equivalent of a guy still brushing his teeth with charcoal and a twig. Although maybe that is in again?

The future belongs to credit unions that operate like software companies—testing, learning, iterating.

dotData isn’t just a tool. It’s your Chief Hygiene Officer for portfolio risk.

Ready to clean up your act?

Explore dotData for Credit Unions →

The post Don’t Get Caught with Your Guidelines Down: Why AI-Driven Portfolio Hygiene Is the New Risk Management Superpower appeared first on CU 2.0.

Fortunately, credit unions have access to data—sometimes, that includes data from other credit unions. Yet, having data and leveraging it are two completely different issues.

That’s where data analytics comes in. Data can provide insights, drive efficiency, solve specific problems, and power digital transformation. If your credit union is getting into data analytics—or switching providers—this post is for you.

Check out our downloadable Credit Union Provider Guides here!

Download the full guide here:

Data Analytics Trends and Insights

1. Why credit unions need data analytics providers.

From checking and savings account data to mortgages, auto loans, HELOCs, credit cards, and insurance… and email correspondence, branch visits, social media comments, call center disputes… member survey data, geographical location, demographics, credit scores…

At some point, it’s best to understand who your members are, what they want, and what they’re eligible for. You’ll learn who’s at risk of leaving, who wants a better credit card, and who’s eligible for a mortgage refinance.

Operations, marketing, HR, lending, and other departments can all use this information to create a better, more engaging, more relevant, and more rewarding member experience.

But to make positive changes, they need that information. And that information comes from data analytics.

Data analytics providers can help you aggregate, clean up/normalize, store, process, and use your data. They’ll show you what your credit union looks like now, what it will look like on its current trajectory, and even what you should do to change it. And, to make it easy, they’ll give you charts, graphs, and other visualizations and reports to make the data easy to understand and act on.

Decisions based on guesswork can get you pretty far—especially if the guesswork is done by a longtime industry professional with a keen eye to the future and the influence of technology. But decisions based on information will always be superior to guesswork.

Data analytics providers will allow your credit union to make informed decisions and transform your credit union, from the internal culture to your roadmap, to the member experience.

2. To warehouse, or not to warehouse?

We’ve separated vendors into two major categories: those that provide a data warehouse (thus offering scalability and a single source of truth), and those that don’t (providing spot solutions with your data).

We think of these as holistic and targeted solutions, respectively. Holistic solutions with a warehouse provide more flexibility for the future—you can build, buy, or borrow spot solutions as needed. Targeted solutions without a warehouse offer more focused guidance for specific strategic concerns. Hybrid approaches are possible!

If your goal is long-term growth, we generally recommend warehouse solutions. Getting the infrastructure, owning your data, and understanding the process is key to developing a data-driven culture. Plus, when you send away for analytics, it gets manipulated and merged, and you lose the source of your data.

One caveat is that we recommend against buying a data warehouse from a company that doesn’t specialize in data warehousing. Sure, it might work and be cheap, but it’ll lack in quality and won’t support your long-term success.

There are also non-warehouse solutions. For smaller credit unions, or those who simply need a few quick answers or solutions, and those who don’t want to develop a data-driven culture, non-warehouse solutions are still a good option. You can get actionable insights with minimal work and often with far less time invested up front.

You may also note that we didn’t include providers such as Amazon, Azure, Google, or Snowflake. Although these solutions are technically superior, they have a few major drawbacks:

1. They aren’t designed for financial institutions;
2. They don’t have integrations or connections with common third-party tools for credit unions; and
3. Most credit unions don’t have the in-house expertise to implement, maintain, and maximize the investment in these solutions.

Lastly, it should be noted that some larger providers with massive product suites may be trickier to integrate with products from other vendors. For example, JHA and Fiserv integrate well with other JHA and Fiserv products.

However, integrations outside of their product suite can be far less straightforward. For this reason, we typically prefer vendors that specialize heavily or exclusively in data analytics and who are dedicated to credit unions.

3. What is “data activation”?

If you’re reading through this guide, you’ve likely encountered the buzz surrounding the transformative concept of data activation. But let’s delve beyond the buzz and explore why data activation has become the cornerstone of success for credit unions leveraging fintech solutions.

If data analytics is the process of turning data into insight, then data activation is the next step that turns the insight into a plan (results not guaranteed).

Data activation is the dynamic process that bridges the gap between insights and action. It’s the secret sauce that transforms your data into actionable strategies, workflows, and initiatives. From powering targeted marketing campaigns to next best product recommendations or ensuring complete referral follow up, data activation empowers credit unions to translate insights into tangible outcomes that help them serve members better and faster, all while increasing revenue.

It’s why you wanted data analytics in the first place. 

Why is this shift towards data activation so crucial?

Because in today’s hyper-competitive landscape, time is of the essence. It’s not enough to simply gather data and generate reports. What truly matters is the ability to act swiftly and decisively based on those insights so you can serve members better. Without data activation, credit unions risk being inundated with charts and graphs that offer little more than a snapshot of past performance.

4. Don’t forget your data strategy

Many of the vendors listed here will help you with your data strategy. However, not all of them will be there to guide you from Point A to Point Z.

If you’re working with a more hands-off provider, a solution that doesn’t include other integrated products, or a vendor without a warehouse and/or strategy solution, then do this:

Bring on a data strategy consultant.

That goes double if you’ve decided to go the DIY route.

Many of the vendors above will include data strategy as an add-on service. Their add-on services will be pretty good because those consultants will be familiar with the platform you’ve chosen.

However, we’d also recommend looking for an independent consultant, such as Anne Legg of THRIVE Strategic Services (she literally wrote the book on credit union data analytics strategy). Anne Legg has helped hundreds of credit unions understand and build out mature data analytics programs. She’s also spoken at many of our events, and she is always a hit.

Evaluation Strategies

There are many ways to evaluate data analytics providers. Especially if you’re just starting your data analytics journey, your first step should be to figure out what you want to accomplish with analytics.

Simply using data-driven technology isn’t a goal in and of itself—rather, goals are things like improving interchange income, increasing auto loan portfolio size, and reducing attrition.

You’ll need to figure out what kind of projects you plan to augment with analytics. Knowing your goals will help you choose the tools best for achieving them.

In the past, we ranked data analytics vendors by 3 different variables:

• Data sophistication
  • Normalization and composability
  • Descriptive, predictive, and prescriptive abilities
  • Connections and integrations
• Support
  • Training, support, or management
  • Access to existing models
  • Credit union focus and understanding
• Ease of use
  • Time to deploy and gain insight
  • UX/UI and ease of use
  • Visualization and reporting

We have a more extensive exploration of the above in another section (see our Definitions and Explanations section).

However, most data analytics providers you’ll find are quite adept at all of the above. We’d rather leave it to you and your data team to quantify the specifics of each vendor.

In light of that, we encourage you to think about the entirety of the provider’s data analytics solution. You might be buying just a piece of the puzzle, or you could find yourself with the puzzle, the table, and a partner to complete it.

Rather than diving into data sophistication, support, and ease of use, we want to provide some evaluation questions and considerations that may help you narrow down the field to find the best fit partner.

Evaluation Questions to Ask Data Analytics Providers

Not all data analytics providers offer the same services, nor do they deliver their solutions in the same way. As open-ended as data analytics can be (in that they are often platforms that allow credit unions to learn various things, limited only by creativity and need), there are nevertheless significant differences between providers.

Consequently, you may want to ask potential providers some of the following questions to see whether their responses fit your needs.

Information access

Once it’s in their warehouse/lake, many providers effectively own your data and will charge you for access to it. What is the provider’s access policy? What do they charge?

Support and training

Does the provider offer any support or training? Can they provide a fractional data team for you if you don’t have the right personnel at your credit union?

Real-time capabilities

How long does it take to do anything with your data? Do you have access to real-time data or are you always working with historical data?

What isn’t included?

Your data analytics platform is one piece of your total data strategy and ecosystem. Are you purchasing your data analytics platform a la carte, or are you looking for a more holistic solution? What other peripheral tools, products, services, or solutions will you need to include in your data analytics purchase? Is there support for indirect lending? A CRM? SEG or onboarding management? Compliance? Does it take you all the way to data activation, or does it stop just short?

Total cost of ownership

Does the price reflect service storage costs? Data access? FTEs to support its use? Additional products or services you’ll need to accomplish your data goals?

The Data Analytics Provider Scoring Guide

CU 2.0 scores fintechs and providers based on what we believe are the 4 pillars of the credit union ecosystem:

• Income statement: (1) Non-interest income and/or deposits, (2) interest income, (3) both
• Balance sheet: (1) Deposits, (2) loans, (3) both
• Member impact: By members affected: (1) 1–33%, (2) 33 – 66%, (3) 66–100% and/or memberization
• Employee impact: (1) Improves workflows, (2) automates some, (3) automates a lot

We score each pillar on a scale of 1–3, although you may see the occasional 0 or ­N/A when we think it’s appropriate. Additionally, in some guides (like this one), most of the providers will have very similar scores.

Our ratings don’t necessarily correlate to quality, nor do they suggest which solution is best for you. Fintechs with higher scores aren’t automatically better, or a better fit, for your credit union and members. Additionally, please note that these ratings are estimations based on our understanding of the product or service.

Trusted solutions are highlighted with an asterisk—these are providers that CU 2.0 has vetted or worked with personally.

Data Analytics Vendor Guide (with Warehouse)

Data Analytics Vendor Guide (No Warehouse)

Data Analytics Definitions and Explanations

There are many ways to evaluate data analytics providers. Especially if you’re just starting your data analytics journey, your first step should be to figure out what you want to accomplish with analytics. Simply using data-driven technology isn’t a goal in and of itself—rather, goals are things like improving interchange income, increasing auto loan portfolio size, and reducing attrition.

You’ll need to figure out what kind of projects you plan to augment with analytics. Knowing your goals will help you choose the tools best for achieving them.

We’ve ranked data analytics vendors by 3 different variables:

• Data sophistication
  • Normalization and composability
  • Descriptive, predictive, and prescriptive abilities
  • Connections and integrations
• Support
  • Training, support, or management
  • Access to existing models
  • Credit union focus and understanding
• Ease of use
  • Time to deploy and gain insight
  • UX/UI and ease of use
  • Visualization and reporting

This section goes into detail about the metrics by which we ranked the data analytics criteria above. Our scale measures from 1–3, with 1 meaning “not great,” 2 meaning “average or good,” and 3 meaning “great or market leading.” Scores of 0 are possible, but they likely mean the provider doesn’t provide a critical component of the criteria.

1.   Data governance and sophistication

Normalization of data is critical. Essentially, this is the quality and integrity of data. Is it well organized? Is it accurate? Is it accessible? And, is it… extensive?

If you’re going to make data-driven decisions, then you want good data. Think of normalization as the performance of your data.

Composability is also key. In short, this is how easy your data is to work with. If you think of your data as a sandbox of possibilities, you can create more solutions with composable, wet sand than you could with messy, dry sand.

Descriptive, predictive, and prescriptive analytics fit different needs. Descriptive analytics are table stakes—any provider should be able to give you insight about what’s happening with your members and at your credit union. Predictive analytics will help you with issues like CECL. And prescriptive analytics are that cherry on top that tells you what actions to take based on what the data reveals. The ability to handle all three types of analytics is fantastic, but not standard.

Connections and integrations ensure you can use data from multiple sources. If you want to use member survey data, CRM data, and other non-core sources of data, you’ll want a provider that can work with as much of your tech stack as possible.

2.   Support

Training, support, and management can be the difference between a vendor you love and a vendor you hate. Does the vendor train you on how to use their solution? Do they provide that training to all staff who need it? Is the training long enough, and is it hands-on or through cumbersome videos and instruction manuals? Are they invested in your success, or do they leave you to your own devices after you cut the check?

Additionally, attentive, ongoing support is a plus. And finally, is the vendor is willing or able to manage your data platform, in whole or in part? It’s rare, but we love the flexibility that provides for credit unions under the $10b mark.

Access to existing models is absolutely necessary. You’ll be better served by a provider that can get you up and running quickly with the solutions you need. Loan origination models, predictive analytics for CECL, marketing segmentations and solutions, KPIs for loan officers…

If every vendor you speak with has existing solutions, make sure they have relevant experience addressing your short- and long-term goals.

Finally, a focus on and understanding of credit unions is key. Many data analytics providers serve a broad range of industries or financial institutions. But credit unions have different cultures, goals, and needs than others—your vendor should be familiar with them.

We prefer vendors that prioritize credit unions, and we particularly appreciate CUSOs over major fintechs that shoehorn data analytics into much larger product suites.

3.   Ease of use

Time to deploy and gain actionable insight is a big consideration. Financial technology is evolving rapidly, and machine learning and other AI are accelerating that. The longer it takes to implement a data analytics solution, the further behind your credit union gets.

However, while we give points for speed, it’s not the most critical component here. Solutions without warehouses, training, and thorough onboarding processes will be slower… and won’t be as comprehensive or scalable.

UI/UX and general ease of use are important. Although ongoing training, support, and management can make bad UI/UX or complicated platforms bearable, you’ll get a lot more out of something that looks good and feels intuitive. You’ll also be able to train new employees faster and more effectively.

At the end of the day, it’s better to want to use the platform rather than have to.

Visualization and reporting should not be underrated.

Most people are not data scientists. Instead, they rely on intuitive user interfaces, dashboards, and visuals to understand data. Is the data readable? Is it accessible? Does the readout make sense to the people using it?

Data analytics vendors that report data in intuitive, intelligible, comprehensive ways will make it easier on your team. You’ll spend less time wondering what the numbers mean, and more time deciding what the numbers are saying. And “time” is key here—if you’re running dozens of reports per month, you’ll want that process to be simple and effective as possible (automation and good design can work wonders)!

Conclusion

There are several vendors to compare, here. And we’ve certainly included enough metrics by which to measure their platforms and services.

The first step is to narrow down the list to a few who look promising to your credit union. Then, we recommend finding vendors who hit your criteria and doing further research. It’s important to choose a data analytics vendor who fits your credit union’s needs and project goals. After you’ve narrowed your list, it’s time to do some demos and move forward.

If you want to discuss any of the providers with a 3^rd party, CU 2.0 would be happy to share what we know. Book a 30-minute consultation with us at no cost here:

Join our fintech call program

Each quarter, we’ll review and discuss fintechs you’re looking at. We’ll also introduce other fintechs that may be of interest!

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The post The Credit Union Data Analytics 2.0 Provider Guide appeared first on CU 2.0.

But new technologies and trends have narrowed the gap between the credit union and fintech/big bank experiences.

Read on to see what we mean—and how organizations like Datava keep credit unions competitive.

The Credit Union Advantage Is Gone

For the last several years, major polls have shown that the experience gap between credit unions and banks has closed. Average bank NPS recently passed that of the average CU. Banks and fintechs are offering better loan rates and faster approvals.

Furthermore, mobile banking is fast becoming the primary banking channel for consumers under the age of ~55. Major banks and especially fintechs are debuting the best mobile apps and experiences.

Kirk Kordeleski, former CEO of Bethpage CU and partner at PARC Street Partners says credit unions must think differently about their relationship to members—and data:

“Member value was about personal, one-to-one interaction. That has changed. The value today comes from data creating that personalization.”

Credit unions generate tons of data. Harnessing that data is key to creating more relevant, personalized financial journeys for members.

To be clear:

To provide value for members, you must personalize their experience. And to do that, you need data.

How Datava Personalizes Member Finance

For credit unions, generating data is the easy part. Actually using it is harder. Fortunately, companies like Datava simplify things.

Datava is, in essence, an end-to-end data activation platform for credit unions. (They also have a full CRM for credit unions.) Datava gives credit unions a full picture of each member—where they get their money, how they use it, what they might be able to do with it…

But unlike other CRMs, Datava fully embraces personalization. They let credit unions customize the look, feel, and end goals of the platform. They also empower credit unions to guide and even automate individualized member journeys according to that data.

There are many credit union CRMs out there, but we love these things about Datava:

1. It’s fully managed. If your credit union doesn’t have the resources to run a new product, Datava will step in to help you meet your goals.
2. It’s fast and affordable. The setup is remarkably fast, and it comes in at about half the price of its competition.
3. It’s extremely flexible. Not only is it customized to each credit union, but it can be used by everyone, from tellers to the CEO.

At CU 2.0, we believe that technology and data are the biggest differentiators in the banking experience—not just in 10 years, but today. And companies like Datava prove it with measurable ROI.

In a world where competitive services aren’t enough, personalization is your competitive advantage. It’s time to embrace data!

Additional Resources

Like what you’ve seen so far? Sign up for our Fintech Call Program and get a personalized, 30-minute call each quarter. We’ll discuss the latest technologies and solutions, make key introductions, and offer early access to events, giveaways, and more!

Learn more about Datava here:

The post Datava, Data, and the Power of Personalization appeared first on CU 2.0.

Sometimes, a fintech comes along and changes the entire way we think about something. That’s what Cinchy is doing with credit union data.

Apologies in advance for anything remedial. We think it’ll be worth it if you’re ready to take the next step in using your data (instead of just having it).

Your Data Is Siloed

Credit unions produce a huge amount of data, and that data can power big strategic wins. But all that data comes from countless fragmented sources. And, unless there’s a solution in place, all that data stays locked in those sources.

The best solutions until now have been data warehouses and data lakes. Whether in-house or outsourced, these solutions try to improve data management and strategy by ensuring that data from different sources ends up in one place, rather than silos that never see each other.

For remedial purposes, we’ll clear up the difference between data warehouses and data lakes here. It’ll be important in a minute: 

• Data warehouses are storage spaces where data is collected, transformed, and structured from different sources. They are excellent for analyzing historical data, but they require data to be cleaned and formatted first. This is tedious, expensive and requires a lot of maintenance. They are rigid in design and require a heavy dependency on IT teams for new reporting requests centralizing all demand for your CU.

• Data lakes store vast amounts of raw data in its native format. Although it still requires maintenance, this approach offers more flexibility as far as what types of data (and how much of it) can be stored. However, because the data is unstructured, it’s harder to draw insights and get value out of the data.

It’s true that both solutions can store all of a credit union’s data in one place.

But people claim that warehouses and lakes reduce silos. That isn’t altogether correct.

The reality is that data warehouses and lakes store all the silos in the same place. The data may be grouped together, but it’s still siloed. It’s still separate. It still requires a lot of time-consuming integration and management to connect that data as more and more silos creep up.

But that’s where Cinchy comes in.

How to Actually Eliminate Data Silos

Cinchy bills themselves as a data collaboration platform that can liberate credit union data. Their technology allows data from different sources to be commingled. This allows a seamless flow of data across sources and departments, eliminating barriers, silos and drive to self-service where all employees in the credit union can leverage data.

There are a few benefits to this approach:

• Better, more complete data. Cinchy eliminates the need to transform, structure, and integrate credit union data. In turn, this offers more complete views of members, trends, and opportunities.

• Reducing Integration Friction. Once data is liberated, you can reuse it over and over again. Resulting in less data copies, eliminating future integration costs and using data to pay it forward. 

• Real-time insights. Because data is designed to seamlessly connect to core applications, it unlocks the ability to now bring real-time capabilities to your CU replacing nightly batch processing in order to access data. 

• Future-friendly architecture. Thanks to its ability to deliver more complete, real-time data, Cinchy is positioned for the future of fast-paced technology, strategy, and fintech solutions.

• Two-way data flow. Improving data every step of the way. Most analytical solutions are read-only and 1 way. With Cinchy, you have a place for even net new data! Resulting in eliminating future silos as well. 

The downside of discussing any data solution is this:

Data—from storage, to management, to how it’s used—is simply a tool that credit unions use to accomplish their goals. We can’t say, “Cinchy does this tangible thing for your members, your balance sheet, your staff…” It’s too variable.

Rather, we can say, “Cinchy gives you the complete platform to make better, faster decisions than other solutions.”

What’s most important here is that Cinchy doesn’t just group data silos in one place; they actually eliminate data silos.

Use Cases for Cinchy’s Data Liberation

If you’re not feeling creative about what Cinchy’s solution can do (or what data liberation means), here are a couple things we’ve learned:

In one instance, a credit union saw that they were about to lose $1 million in deposits to another financial institution the next day. Within minutes of the seeing the pending transaction, that credit union was able to guide that member to reinvest that money and more, netting $1.7 million in deposits with a phone call.

In another instance, a credit union considered a daily dividend increase to drive deposits. After examining their trend data, they found no benefits to their members and scrapped their dividend increase, which saved them $180k in annual expenses.

If you’d like to learn more about how Cinchy works, download this:

Additional Resources

Like what you’ve seen so far? Sign up for our Fintech Call Program and get a personalized, 30-minute call each quarter. We’ll discuss the latest technologies and solutions, make key introductions, and offer early access to events, giveaways, and more!

And of course, please subscribe to our blog (if you haven’t already)! 

The post Cinchy Wants to Change Credit Union Data by Liberating It appeared first on CU 2.0.

Credit unions have been historically slow to react to technological innovations, but that could change with data. Data analytics can help to monitor, operate, and enhance the member experience.

That is, if they can access all the right data, enough of it, and then use it to create a roadmap that leads to implementing real change across the organizations. Outdated technology stacks, decentralized data, and a lack of resources could be difficult hurdles to overcome as financial institutions start down the road to analytics.

Experts from TerraStrat, OM Financial Group, Trellance, and Datava join facilitator Chris Otey, CRO of CU 2.0, on the panel at the CU 2.0 Brainstorm Event to consider solutions and issues they foresee as credit unions begin looking ahead to a world where data analytics are an integral part of the business.

Watch the video below to catch the whole thing, or read on for a short summary.

Changing Culture Around Data within the Credit Union Industry

Karan Bhalla, CGO at Trellance opens up the discussion with a concern: how do credit unions  determine what tools and services are most applicable when looking at analytics provided by fintechs? Credit unions are often lacking in resources and need to know how to make the biggest impact with the smallest investment.

In the long run, everyone is going to need a data warehouse regardless, but in the meantime, are you able to utilize just enough of what you are investing in to see a return?

Kirk Kordeleski, Partner at OM Financial Group, says the dynamics of the credit union are dramatically different than for-profit institutions. Without stock, there hasn’t been a culture of using data and analytics driving strategy and goal setting within the industry. Now that the culture is changing, it’s difficult to catch up and use the data effectively, and credit unions need to get an urgent roadmap planned for the next several years. Institutions need to be utilizing data as decision making criteria and talking about it as often as possible, while implementing new technology at the same time, to ensure that the system is centered on decision making rather than simply reporting.

CEO of Datava, Gordon Flammer, outlines a difference between research and execution. Credit unions require both sufficient data and analytics to be effective in impacting member experience, as using bad data can worsen the member experience. Regardless, if you aren’t implementing positive changes based on good data, the whole system is rendered useless, especially where training high-turnover staff is concerned.

Ray Wachauf, CEO at TerraStrat agrees with Flammer, stating that companies should be creating routines around data, assessing for viability, and understanding how to treat the data. Stable models where data can speak for itself take a while to achieve, but careful application of solid data science and getting people with experience is the biggest hurdle.

The Age of AI and New Analytical Expectations

It’s necessary to understand how members are interacting with interfaces throughout the member experience, as Wauchauf points out. Finding out how they are using the credit union, what is working for them, and where to improve the experience to make the biggest impact starts by asking questions that lead to data points:

• How are they using online banking and mobile apps?
• Where are you losing members in the experience?
• What can you gain from that information?

He points out that not many institutions are capturing the data and using it in a meaningful way.

For Flammer, it brings to mind the difference between the “Terminator” generation and the new AI-friendly generation: now, consumers want predictive analytics to suggest relevant products and services, whereas older generations were far more wary of data being tracked and recorded. We can now use it to identify opportunities to develop new or better products and services for members to improve their experiences.

The most improvement comes from using data scientists and engineers to look at the data and determine if you have enough to make decisions and the predictions they are making, according to Kordeleski. Institutions aren’t currently using the data effectively as a tool to improve services.

Further supporting the point that Flammer made about the differences between the generations, Bhalla highlights a shift in expectations. Younger people are not only more comfortable with their data being used, they now want you to know their needs and desires ahead of time, shifting interactions from real-time to being as proactive as possible. To get there, credit unions have to use data faster, whereas they’re currently behind the curve. The challenge, it seems, is to help credit unions get ahead to actually use the data in a timely manner.

To Kordeleski a cultural transformation and strategic prioritization is critical. Embracing data in strategy and decision making can benefit credit unions, but it needs to be implemented efficiently and urgently.

Centralizing Data to Get an Accurate Picture

How to help consult to develop that roadmap in a way that will happen quickly that will turn over short term gains and long term gain competitive advantage

Skillset and budget has to be significant to make it happen, well, with outside expertise. Area needs to be centralized, not dispensed to a lot of people part time, cultural and needs to be organized correctly in order to do it well.

Flammer urges credit unions and their tech partners to start building models now. Any model can be improved, so perfection doesn’t matter when it comes to getting something in production. There are three key areas to focus on in production:

1. Collecting and curating the data
2. Analytics
3. Creating good models and actually utilizing them

Collecting and curating the data seems to be an industry-wide challenge that needs to be addressed before implementing strategies in order to get an accurate picture of the member experience. As Wauchauf points out, most CUs don’t have integrated cores and the data isn’t interacting across all products and channels.

Both Kordeleski and Wauchauf back this up, offering their own experiences working with credit unions as further proof that something needs to be done to integrate data. Most credit unions use upwards of 30 different databases to store sets of data, and people within the same department store data in different places.

Flammer further solidifies the idea that data consolidation is important, stating that credit unions need as much useful curated data as possible, because one data point could mean the difference between predicting whether you will retain or lose members.

Further Discussion

The panel opens up the floor for audience members to contribute to the conversation. It’s mentioned that credit unions have a lot to benefit from data being housed collectively and then going to AI applications.  Partnering may be a more viable option to institutions with limited resources, leveraging the power of credit unions to share data may be where the future is headed.

Joe Keller from Visions FCU reinforces this point from the audience, expressing that he also believes that the collaborative use of data is a largely untapped resource, as credit unions typically have more data than most financial institutions.

The consensus is that the challenge that may be the most worth overcoming is creating a collaborative environment within the industry, allowing credit unions to partner together to solve common problems and garner quick wins for their members rather than waiting around for startups and fintechs to do it for them.

The post How Can Credit Unions Use Analytics to Improve the Member Experience? appeared first on CU 2.0.

However, we understand that the plethora of vendors can make it hard to find the right one. Moreover, even reviewing our data analytics provider guide won’t give you the answer to your ultimate question:

Which data analytics provider is right for my credit union?

Unfortunately, the answer to that question will depend on your credit union’s size, goals, and culture. Yet, the best way to find the answer to that question is to ask a series of other questions.

Here’s what you should ask potential data analytics providers for your credit union. And keep scrolling for our vendor recommendations below!

Data Analytics Provider Questions (and Reasoning)

First, we’d like to thank Arkatechture for their insight. Arkatechture is a CUSO providing data management and business intelligence solutions and services for credit unions. Their data warehouse platform, Arkalytics, combines a data lake with a suite of financial dashboards and customizable data studio for the credit union’s builders and designers.

Arkatechture has spoken in some of our events and provided fantastic guidance in the past. This blog of theirs exemplifies the type of information we expect all vendors to provide.

And now, the questions:

1.   Do you have a data warehouse?

Vendors that offer a full warehouse (or lake) solution future proof your data analytics capabilities. You get a single source of truth that is vetted, governed, and defined—it’s all standardized and normalized.

Warehouse solutions allow you to scale and build, buy, rent, or lease any number of descriptive, prescriptive, and predictive solutions from your data.

Conversely, providers that don’t offer warehouses usually have a shorter onboarding time because they simply work with the data you give them. Plus, they’re good at creating spot solutions to solve specific problems, such as member attrition and industry benchmarking, but they may not give you the same flexibility and customization to address emerging challenges.

2.   How long does it take to deploy (and gain insight)?

The faster you’re able to get up and running, the sooner you can begin to solve real business problems with data analytics. Especially if you’re trying to hit specific goals on a timeline, time to deploy could be a major consideration. Less time onboarding = more time improving.

One thing to be wary of in the promise of a lightspeed deployment is understanding exactly what work the vendor is accomplishing during the implementation. Ask these questions to dig deeper before comparing the promised speeds, especially if you’re evaluating a data warehouse solution:

• Are they just setting up the database itself and we must configure everything?
• Are they mapping and connecting all our sources to the data model or just the core?
• Are they setting up proper data governance best practices so we can trust the results?
• Are they building it for our needs today or planning for growth in the future?

3.   Do you have existing models that we can use?

It’s hard to imagine a competent credit union data analytics vendor that doesn’t have some models ready for use. However, some might provide use case models that more closely fit your goals.

Ask about which models they have that fit your intended use case(s). Also ask how extensive their library of use cases is—it may be useful soon (and will save time in the long run). ). If they don’t solve for every use case out of the box, what options will your team have to define and build their own use cases?

4.   Do you provide training and support?

Especially for vendors that provide data warehouses (and not just spot solutions), training and support are critical. What’s the point in buying a data analytics solution that nobody uses? Some vendors can even help you measure and analyze user adoption by looking at logs to determine which users are interacting with your dashboards and models the most. This can help surface up opportunities for additional coaching and training for your internal users.

Training is critical to building a data-driven culture and ensuring buy-in across all departments and staff. Support is key to building out dashboards and solutions, learning new strategies, and getting un-stuck.

You may also look for ongoing support in the form of user groups, knowledge hubs, documentation, training videos, and professional services when you need more firsthand development support.

If they do provide training and support, ask for how long.

5.   What kind of data can you integrate?

The more data, the better. Most providers are very adept at pulling in data from a wide variety of sources. (For example, can they incorporate CRM data? Member survey data?) And, so long as that data can be normalized—and the process isn’t clunky—you’re good to go.

Ask about their integrations—it would be a bummer to learn that your preferred vendor doesn’t play well with parts of your stack.

6.   What does your solution look and feel like?

This covers a lot of ground. For example, you might want to hear about:

• Reporting and visualization: What does the data look like after you’ve crunched the numbers? Does it make sense? Can you understand what it’s showing you?
• Composability: Creating new models, dashboards, and use cases takes familiarity with the data. How easy does the platform make it for your team? Will you need a data scientist, or does it offer low-code and no-code options?
• Self-service: Can anyone access and understand the data/dashboards/reports? Or is that capability in the hands of an elite few?

The more people who can use it, the better. Intuitive UI/UX helps, as does accessibility (multiple devices, natively or on browser, etc.).

7.   How do you handle security and compliance?

As long as a 3^rd party has access to your systems and any member data, you’ll want to make sure there’s no issue. You may find that some vendors go above and beyond here, whereas others meet bare minimum requirements.

Who Should You Ask?

Our credit union data analytics provider guide has a more complete list of vendors and information. However, we’re comfortable recommending some of our favorite vendors here. You will notice that all specialize specifically in credit unions (most are CUSOs) and data analytics (are not part of larger tech providers):

Warehouse / lake data analytics providers for credit unions:

1. Datava emphasizes data activation. After all, what’s the use of a data analytics platform if it doesn’t connect to anything and you can’t do anything with it (unless you spend all your time building your own modules)?
2. Cinchy changes the way credit unions access their data, in many cases enabling real-time data analytics capabilities that allow credit unions to respond immediately to opportunities.

Non-warehouse data analytics providers for credit unions:

1. Crux Analytics helps credit unions cater to small business better. And we mean way better.

Additional Resources

Like what you’ve seen so far? Sign up for our Fintech Call Program and get a personalized, 30-minute call each quarter. We’ll discuss the latest technologies and solutions, make key introductions, and offer early access to events, giveaways, and more!

And of course, please subscribe to our blog (if you haven’t already)!

Learn more about Datava here:

The post Questions to Ask Data Analytics Providers (for Credit Unions) appeared first on CU 2.0.

An increasing number of credit unions are working with data analytics. And it makes sense:

Data is just another way to know your members.

But as more solutions hit the market and the cost of entry falls, new trends are showing up. See what other credit unions and vendors are working with as they dive deeper into the world of data.

Join the next Brainstorm Event here: https://cu2.wpenginepowered.com/brainstorm-event/

1.  Credit Unions Trying to Do Too Much

As credit unions develop new solutions and work with new fintechs and vendors, they increase the amount of data in their ecosystem. And with more data comes the desire to do more things with it.

The biggest challenge for credit unions is organizing all their data before working with it.

But they shouldn’t do that, says Anne Legg, Founder of THRIVE Strategic Services. Instead, she advises credit unions to prioritize member needs over data organization. “focus on the most important thing… the member.”

Find out where your members experience friction. Then, find the data that relates to that friction. Work with that data and don’t worry about organizing the rest.

2.  Composable Data

Composable data ties together data from various sources. It allows for low-code and no-code solutions to work and is generally user-friendly.

COVID-19 brought big technology, behavior, and strategy changes. There’s a good chance that data analytics for credit unions will be fundamentally different going forward. Composable data allows for those changes—and the changes that are sure to come from AI and other disruptive trends.

3.  Better Knowledge About the Community

The best thing about credit unions is that they serve communities. And the best way to understand the community you serve is through their data. Data is starting to help credit unions with:

• Seeing where to put (or remove) branches
• Broad community trends (e.g. demographics or industry changes)
• Tracking shifts in member needs

For example, let’s say a credit union serves a SEG. What happens if a huge number of layoffs occur? How do credit unions retain those members and support them through their transition?

Credit unions already have a lot of information about individual members. Learning more about the community is the next big step.

4.  Data Powers Vendor Solutions

Customer Relationship Management (CRM) platforms are gaining traction with credit unions. Data is fueling the effectiveness of CRMs, allowing credit unions to gain deeper insights about members. It also pushes better content workflows and nurturing sequences in marketing efforts.

Additionally, data also powers AI products, which are increasingly prevalent in credit union ecosystems. There’s an AI version of almost every tech product out there. And if something doesn’t have AI yet, it probably will soon.

Data will increase the efficiency and accuracy of AI. And, because AI helps to scale, data will be key to the success of new solutions.

5.  Warehouses Preferred

There are two general strategies when it comes to data analytics for credit unions:

With a warehouse or without.

As more credit unions get comfortable with data—and as it gets easier to work with—credit unions are leaning towards solutions that include warehousing over those that don’t. The most “holistic” approach to data provides more options for the future.

More About Data Analytics for Credit Unions

CU 2.0 produced a credit union data analytics vendor guide to help with evaluation. And we’re working on a new one shortly, which you will be able to find with our other 2.0 Guides here.

Subscribe to our blog to be the first to hear about our next Brainstorm Event, which will feature more discussion about trends, use cases, and solutions in the world of data!

The post Credit Union Analytics: Current Trends, Solutions, and Use Cases appeared first on CU 2.0.

Credit unions began by closing branches, leaning heavily on their digital channels… and continued to lean on digital even after reopening. Now, more members than ever use online and mobile banking. Branch visits are fewer and farther between.

All of this raises the question: How can credit unions continue to be there for members when they can’t physically be there? How can your technology create the same friendly, safe experience that in-branch visits typically provide?

Read on to see eight technology trends that credit unions should keep a close eye on (especially in the face of the coronavirus).

Want the latest developments in technologies and fintechs? We provide free, personal calls to upper management about market trends and innovations. Contact Chris Otey to learn more.

Major Credit Union Technology Trends

Trend adoption—or technology adoption—in the “real world” is a bit faster than with credit unions. If it were a city, Credit Unionville would be downright quaint and wholesome by comparison.

But that’s not to say that credit unions are technology averse. Some things just take a bit longer to get up to speed, and our industry’s gears are just starting to turn faster. In fact, here’s a look at the state of things as of a little over a year ago:

1. Cloud computing

When the pandemic first hit, IT departments had to enable dozens or even hundreds of remote users in a matter of weeks. It took them away from valuable digital enhancements, development projects, service technology improvements, and their usual to-do lists.

Cloud credit unions were able to transition to remote work in hours.

Yet credit unions continue to lag in cloud adoption. To be frank, this doesn’t make sense. Licensing is easier, business continuity is simplified, and you get easier access to enterprise-level automation.

What’s crucial to understand about the cloud is that it serves as the foundation for most other credit union technology trends. If any of the following are part of your road map going forward, you’ll need to start with the cloud:

• Machine learning and AI
• Digital transformation
• Faster disaster recovery
• Robust security and SIEM

And that’s just the beginning. If you told any younger generations that most credit unions aren’t on the cloud, they would roll their eyes, shrug, and say, “maybe that’s why we choose big banks and fintechs.” Maybe. But they would definitely be surprised. Considering that the oldest Millennials are almost 40, maybe it’s time to pay attention to what kinds of technology they expect to come standard…

Operating on the cloud drastically simplifies remote work. And, while Covid-19 revealed the necessity of work from home capability, it also suggests a real possibility: that remote work will remain a large part of the future. CU 2.0, like so many other tech companies on the West Coast, is embracing the work-from-home lifestyle.

So, if you want to stay competitive and open doors to opportunity in the future, cloud computing is where to start.

2. Digital transformation

Before the pandemic hit, the American Bankers Association found that 82% of people preferred digital over in-branch banking. That number has certainly grown since March. Until our industry puts 82% of our service efforts into digital channels, digital transformation should be a top priority.

Credit unions that don’t prioritize rapid digital transformation today may not be here tomorrow. Regular, in-branch banking is hardly a thing anymore. In a way, digital transformation is more like “digital survival.”

Here are a few things that should absolutely be on your checklist for 2021:

1. Online banking
2. Mobile banking
3. Online appointment setting
4. Marketing automation
5. Online account opening
6. Digital vehicle buying services
7. Digital identity
8. Digital loan closing
9. E*Notary services
10. AI chat bot

Although it’s a good checklist, it is just a list. You could check off every item and still not have a cohesive or enjoyable member experience. It’s important to keep usability, analytics, SSO, automation, a CRM, and more to ensure a fluid, fresh, and relevant experience in 2021.

The checklist is just tactics—you need strategy. We’d be happy to help you craft and execute that strategy.

3. Fintech partnerships

This year, CU 2.0 began our Credit Union–Fintech Mastermind to foster a community of innovative CU and fintech leaders to focus on collaborative problem solving. It has opened up partnership opportunities, increased adoption speed, and broken through some of the typical industry complacency.

It also revealed that most credit unions are inundated with new fintech solutions daily. They simply don’t have the time or energy to review each new technology or company that comes across their desk. One industry CEO suggested that fintechs must solve multiple problems if they want to truly stand out.

We can’t tell you which fintech partnerships would be most valuable to you, but we have a short list of recommendations:

1. List issues you can’t solve internally. Ascribe a monetary value for it and keep an eye out for fintechs that can solve it cost-effectively. (Let us know if you need a suggestion.)
2. Join the Credit Union–Fintech Mastermind.
3. Join the CU 2.0 Quarterly Fintech Update Call list (customized for each CU).
4. Book a fintech strategy workshop with CU 2.0 and move from reactive to proactive—take control of your future.
5. Check out ModusBox for information on faster integration of fintechs without relying on your core or online banking vendor!

The honest truth is that most credit unions don’t have quite the time or money to pursue radical, game-changing development projects. If it weren’t for the prospect of fintech partners and vendors, credit unions could be forgiven for just giving up on harnessing groundbreaking technologies.

But there are hundreds (or possibly thousands) of fintechs out there. Each has a novel new product, interface, or service designed to help credit unions and their members. And at this stage in the game, they’re still cost-effective options. (However, as these companies get more established, they raise their rates. Consequently, it’s a good idea to find partners sooner rather than later.)

If you’re not sure where to look, you might start with our Fintech Friday series. If you want to see even more, check out our database, which is is undergoing an extensive revision at the end of 2020.

4. Digital identity

There are many reasons to pursue digital identity and provide it to your members. For starters, a secure digital ID solution can significantly reduce fraud at your credit union. Instead of demanding complex and changing passwords, PINs, and verification questions, digital ID is a simple, streamlined way to increase member ID security.

The other benefit is that it reduces member authentication time by quite a lot. Especially for remote account verification (such as when reaching the call center), digital ID makes the whole process, faster, easier, safer, and less annoying!

We recommend looking at MemberPass, which is already gaining broad industry adoption. Plus, they’re working with companies like the Linux Foundation, IBM, and MasterCard to create a global standard for digital ID.

5. Marketing automation

What can we say about marketing automation that we haven’t already? That it’s becoming standard in almost every industry? That it’s cheaper than you’d expect? That it’s so easy to use, a single person can manage personalized marketing messages for tens of thousands of contacts?

If you want to know who to market to, what to market to them, and when, then you need marketing automation. However, if you like spending a lot of money on ads, wondering who’s seeing them, and sending special offers to people who don’t want them, then skip marketing automation altogether.

Here’s the short version: marketing automation is simpler and more effective than what you’ve been doing. It absolutely makes the list of 2020 credit union technology trends!

But where can you find marketing automation for credit unions? Easy. If your credit union wants more support, customizable modules, coaching, omnichannel capabilities, and built-in compliance features, then we cannot recommend Total Expert enough. They’re the closest thing to a plug-and-play marketing automation solution that the credit union industry has.

6. Analytics

 Credit Unions are definitely working hard on this trend. Within the credit union industry, many credit unions have purchased analytics platforms. Other credit unions have hired data scientists. There are even two distinct credit union analytics conferences now that are phenomenal: AXFI and CUAnalytics.

In 2020, Trellance acquired OnApproach, allowing them to transform with new core and third-party connectors, use cases, and functions. Essentially, they want to ensure that your credit union can actually do something with the analytics.

The industry is aware of the power of data and is actively working to leverage it. A terrific example is CU Rise, which was built on the Trellance analytics platform. CU Rise took data pools, ran them through machine learning, and watched as their initial attrition prediction models went from 6x better to 9x better within a few weeks. That’s huge. Plus, they’ve been creating predictive analytics models to help credit unions grow during the pandemic.

However, analytics go hand in hand with marketing automation. If you don’t know how to use the analytics (or you aren’t prepared to invest in the marketing side of things), then analytics alone won’t do you much good!

7. Artificial intelligence & machine learning

AI is such a hot credit union trend that early adopters probably feel that it’s passé by now. As machine learning progresses, more and more financial institutions and fintechs are using it. And because it’s such a flexible technology, they’re all using it for different things.

JP Morgan Chase finished 360,000 hours of legal clerical work in minutes with their COIN AI. Credit unions have stayed connected with members at all hours during the pandemic with chatbots, which provide almost all the same functions as a call center, but with less overhead. Lenders use AI modeling to better determine creditworthiness and help their members out.

Are people ready for AI?

Yes. Absolutely.

To get a better idea of what kind of AI you might use (and how to use it), here are two easy resources:

1. Buy the new FinAncIal book from CU 2.0
2. Have an AI strategy session with the CU 2.0 team and make sure you are ready for tomorrow!

8. IT Security

The current tight labor market makes it difficult for credit unions to find cyber experts. Furthermore, the NCUA’s focus on SIEM (Security Incident Event Management) continues to crank up the pressure.

And, although the cloud offers many security perks, it still has vulnerabilities. So, to avoid becoming the next Equifax or Capital One, it’s critical to continue ramping up IT security efforts in 2020.

Here are some cool cyber technologies we have been investigating:

1. Breach Clarity (our choice for the coolest and most useful ID and fraud protection technology)
2. CISO as a Service (from Ongoing Operations)
3. Remote Worker Security Assessment (also from Ongoing Operation)
4. FlexPay (card-based machine learning fraud detection and decline salvage)
5. Text phishing detection and remediation

As the world operates increasingly digitally, the risk of cyber attacks grows. Advanced strategies, governance, and tech platforms are necessary to combat risks and comply with regulations.

For better or for worse, IT security will probably never drop from our list of credit union tech trends.

Final Thoughts

So, as we move into 2021, it’s time to spot trends early so that we don’t get behind in the future. Executive teams and boards in particular must begin discussing these technology trends and their impact.

• How has Covid-19 affected your credit union’s needs?
• How has it affected your members needs?
• What problems do you need to solve?
• Can any of them be solved by following one of these trends?
• How might each trend change your members’ perceptions and/or expectations?
• What should your credit union invest in first?

Maybe some of these should go onto our list of credit union strategic planning topics…

Oh, and if you haven’t already, subscribe to our blog! We write about all of these topics. Often.

The post 2021 Credit Union Technology Trends to Watch appeared first on CU 2.0.

Nobody likes getting dinged with fees. In fact, levying too many fees at your members is a surefire way of frustrating them. But what if I told you there’s a way to make your members appreciate your approach to overdraft fees?

There are a few win-win ways to turn a bad member experience (overdraft fees) into a great one. And the best part is, they don’t take much effort. In fact, they may even save time in the long run…

Why Am I Thinking About Fees?

Recently, I overdrafted my account at a credit union. Doh! The funny thing was that I had plenty of money in my savings account, and it was an honest mistake. So, how did this happen?

The core of the issue is that I was a new member and no one at the credit union had ever offered to setup overdraft protection. I assumed it was set up automatically by default. I hadn’t had an overdraft fee on an account in years!

The funniest part of the story is how I found out that I had overdrafted. I found out because I received several delinquent notices on a bill. I had bill pay setup, and the credit union just didn’t pay my bill… because I didn’t have the money in my checking account!

So, once I saw the delinquent notice, I had to take the time to dig in and figure out what had happened. At the end of the day, the credit union ate the $90 dollar late charge and made me whole (although they probably didn’t have to). I felt both guilty and vindicated after complaining.

This experience got me thinking about how often this sort of thing happens and what our industry can do about it. So, I came up with a super simple hack that almost all credit unions can implement right away.

Hack One: Set Up Overdraft Protection for Certain Members

Once per month, run a report on all members who have at least a certain amount of money in their savings accounts. Then, find the members in that group who don’t have overdraft protection set up. (This should be easy for any analytics team.)

And then set it up for them.

That’s it! That’s the entire hack. Instead of charging them a fee and explaining why overdrafting costs them, you can avoid that entire experience right of of the gate.

And here’s how to turn a small win into a big one:

When you set up overdraft protection for your new member, tell the member! Send them an email. You can try this one if you’d like: 

“Dear Member,

We just wanted to take a minute to say thanks. Building strong communities means looking out for each other.

Recently, we saw that you don’t have overdraft protection set up. And although that’s perfectly fine, we think you’d be a terrific candidate for it. This feature allows you coverage so that if you accidentally forget how much money you have in your checking account, we have your back and don’t 

A) accidentally bounce your check(s)

B) charge you a fee

The charge will draw from your savings account instead. If for any reason you don’t want us to set that up, please click here to let us know.” 

Ta da! You have just upgraded the experience, improved your digital prowess, built trust with your members, and put their wellbeing first. Good job! Pat yourself on the back!

I’ve got a couple more ideas though. Let’s save some celebration for them.

Hack Two: Limit Your Overdraft Charges

I spoke with one credit union that had created an awesome overdraft policy:

The credit union will only ever charge you for two overdraft fees in one 24 hour period.

This policy makes a lot of sense when you think about it. Whenever a member was charged a bunch of fees for multiple transactions in a short period of time, the credit union almost always ended up refunding majority of these. However, by limiting the policy to two overdraft fees in one 24 hour period kills two birds with one stone. The member still has to take responsibility for their negligence, but the member service representatives can do them the service of waiving the rest of the balance.

So, although members may still be unhappy about their overdraft fees, they also see that their credit union isn’t exploiting a bad situation. That kind of move builds trust.

Hack Three: Overdraft Education

Here’s another awesome policy being used by a few credit unions today:

On the members’ first overdraft, the credit union automatically alerts the member and refunds the fee. Then, they send a link to a video that educates the member on how to avoid overdraft fees in the future.

The video might explain the difference between available and actual balance. It could provide some helpful budgeting tips. It should introduce other ways to avoid overdrafting, such as setting up overdraft protection. It might even suggest picking up a credit card or a short-term loan for when the bills can’t wait.

This solution lets your members know that you have their best interests at heart. Plus, it also gives them valuable resources to keep them out of trouble in the future!

Final Thoughts

Your credit union probably spends a good amount of time speaking with unhappy members and reversing fees. If you stay proactive about overdrafts and help your members avoid them, you’ll create great member experiences. And you’ll save your representatives many uncomfortable conversations, to boot!

These are just a few thoughts on how to help your credit union deliver a streamlined digital experience that builds trust and wows the member without any fancy systems or tools. (However, if you do want fancy systems and tools, you might want to check out marketing automation…)

Want to learn more about how your fellow credit union leaders are using data? We invite you to join our Credit Union 2.0 Strategist Group where over one thousand industry leaders comment on new news and trends while sharing and learning from one another.

The post 3 Overdraft Hacks That Your Members Will Love appeared first on CU 2.0.

What do you think when you get an alert from your credit union offering you a 2.9% car loan – and you already have a 1.9% car loan that you got two years ago from the same institution?

Duh, right?

Lou Grilli, an executive at payments CUSO Trellance, thinks a lot about exactly those kinds of questions and he believes that for credit unions the solution will come through smarter, faster data analytics.

His opinions are rooted in information gleaned from the experiences of the roughly 2000 credit unions that own Trellance.

What your member will do, or need, tomorrow already is known in today’s data. You just have to look at it.

And Grilli thinks data analytics can unlock tactics for encouraging more member use of your institution’s cards – which is a goal of just about every credit union.

Along the way, Grilli also talks about the future of contactless cards, also what to expect from digital wallets such as Apple Pay.

Payments in the past five years have changed much more dramatically than they had in the preceding 25 and Grilli has a ringside seat.  That’s why he is worth a long listen in this podcast.

Click the photo below to listen now!

Check out other podcasts in the series here!

The post CU 2.0 Podcast 22: Talking Payments and Data Analytics with Trellance’s Lou Grilli appeared first on CU 2.0.