Individual Profitability

Granular customization capabilities for your deepest profitability insights yet.
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Integrations Include:

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Profitability insight tools traditionally saved for only the largest companies

Custom Profitability
Flexible Parameters
Cost Spreading
Transparent Data Dictionary

Profitability at the most granular level

Don’t assume profitability based on “averages of averages”. Connect to your data at the most granular level including interchange on transactions and interest spreads on individual products. 

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CUSTOMERS INSIGHTS

Powerful ML/Al-driven engagement,
segmentation, and predicitive actions

Gemineye data lakehouse metrics summary
Definition Customization
Gemineye data lakehouse metrics summary

Definition Customization

Definition Customization documents fully integrated into the solution and design to ensure your team knows exactly how all fields move from source to the data warehouse to the dashboards and reports.

Advanced Lakehouse Monitoring

Advanced Lakehouse Monitoring

Advanced Lakehouse Monitoring documents fully integrated into the solution and design to ensure your team knows exactly how all fields move from source to the data warehouse to the dashboards and reports.
Gemineye data lakehouse metrics summary

75 integrations and counting

We currently support over 75 integrations – even the ones that other data analytics providers won’t touch. Our integrations incorporate leading credit union and bank solutions, like consumer loan and mortgage originations, digital banking, CRM / MRM, third-party data vendors, and more.

Gemineye integrations infographic

Advanced insights out of the box

With over 250 visualizations available out of the box, day one of Gemineye gives your team – technical and non-technical alike – an immense number of reports and dashboards to consume.
Gemineye data lakehouse deposit segments

Advanced insights out of the box

With over 250 visualizations available out of the box, day one of Gemineye gives your team – technical and non-technical alike – an immense number of reports and dashboards to consume.
Gemineye data lakehouse deposit segments

Unlimited potential

Thousands of fields in our Gemineye Lakehouse coupled with thousands of measures gives your teams a nearly unlimited number of ways to slice and dice your data for maximum effectiveness and usefulness.
Gemineye Data Lakehouse catalog explorer screenshot

Unlimited potential

Thousands of fields in our Gemineye Lakehouse coupled with thousands of measures gives your teams a nearly unlimited number of ways to slice and dice your data for maximum effectiveness and usefulness.
Gemineye Data Lakehouse catalog explorer screenshot

All roads lead to list generation

All aspects of the Gemineye Lakehouse give you the ability to generate lists directly out of your filtered dashboards and visualizations. When it comes to customer communication, the analysis is just as easy as loading these lists and files into your marketing tools and email engines.
Gemineye Data Lakehouse share account listing

All roads lead to list generation

All aspects of the Gemineye Lakehouse give you the ability to generate lists directly out of your filtered dashboards and visualizations. When it comes to customer communication, the analysis is just as easy as loading these lists and files into your marketing tools and email engines.
Gemineye Data Lakehouse share account listing

Hear from Our Clients

One of the biggest reasons we chose Gemineye was their architecture. They provided a Databricks resource within our Azure environment, giving us full control to build and expand our ETLs using a wide range of data integration tools.
Kevin Quinn-Individual Profitability
Kevin Quinn
CIO
NuMark CU
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Advanced insights out of the box

With over 250 visualizations available out of the box, day one of Gemineye gives your team – technical and non-technical alike – an immense number of reports and dashboards to consume.
Gemineye data lakehouse deposit segments

Advanced insights out of the box

With over 250 visualizations available out of the box, day one of Gemineye gives your team – technical and non-technical alike – an immense number of reports and dashboards to consume.
Gemineye data lakehouse deposit segments

Unlimited potential

Thousands of fields in our Gemineye Lakehouse coupled with thousands of measures gives your teams a nearly unlimited number of ways to slice and dice your data for maximum effectiveness and usefulness.
Gemineye Data Lakehouse catalog explorer screenshot

Unlimited potential

Thousands of fields in our Gemineye Lakehouse coupled with thousands of measures gives your teams a nearly unlimited number of ways to slice and dice your data for maximum effectiveness and usefulness.
Gemineye Data Lakehouse catalog explorer screenshot

All roads lead to list generation

All aspects of the Gemineye Lakehouse give you the ability to generate lists directly out of your filtered dashboards and visualizations. When it comes to customer communication, the analysis is just as easy as loading these lists and files into your marketing tools and email engines.
Gemineye Data Lakehouse share account listing

All roads lead to list generation

All aspects of the Gemineye Lakehouse give you the ability to generate lists directly out of your filtered dashboards and visualizations. When it comes to customer communication, the analysis is just as easy as loading these lists and files into your marketing tools and email engines.
Gemineye Data Lakehouse share account listing

75 integrations and counting

We currently support over 75 integrations – even the ones that other data analytics providers won’t touch. Our integrations incorporate leading credit union and bank solutions, like consumer loan and mortgage originations, digital banking, CRM / MRM, third-party data vendors, and more.

Gemineye integrations infographic

News and Resources

sailboat on water POV
Set a Course: Tracking (and Correcting) Your Data Analytics Progress in 2024

Why Data Analytics Matters Data analytics is essential for staying competitive in today’s competitive landscape. A recent study by Jack Henry found that 42% of credit unions prioritize leveraging data ...

Ann Ditlow and bento box
Ann Ditlow: Data Analyst at 4Front CU

Welcome to our very first edition of “A Day in the Life of a Data Analyst,” featuring the equally talented and down-to-earth Ann Ditlow, Data Analyst at 4Front CU. Ann ...

Get to Know Bill Butler, Sr. Power BI Developer & Consultant

Bill has a deep background in the credit union industry. Throughout his robust career in the industry, Bill has utilized technology and data with finance/accounting to help credit unions and banks ...

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common hurdles and challenges for data analytics banks and credit unions

The Top 3 Data Analytics Challenges for Credit Unions and Banks

Creating and managing a data-driven culture at a financial institution is no easy task. Even the most seasoned analytics leaders will tell you that the analytics path is never straight...
READ NOW
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Hear from Our Clients

One of the biggest reasons we chose Gemineye was their architecture. They provided a Databricks resource within our Azure environment, giving us full control to build and expand our ETLs using a wide range of data integration tools.
Kevin Quinn-Individual Profitability
Kevin Quinn
CIO
NuMark CU
Purple quote icon
Purple quote icon
Showing Slide 1 of 2

Individual Profitability FAQs

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How Can Individual Profitability Help Your Team?

Conquer a tough lending environment with tools that reveal opportunities and risks alike

Deliver long-awaited autonomy and flexibility to your finance team

Hard-working analytics for hard-working operations teams

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News and Resources

semantic models for credit unions

[Video]: Semantic Models 101 for Community FIs

Let’s talk semantic models for community financial institutions. There’s a lot of misconceptions about them. In this video, we discuss the basics of semantic models, break down myths, and share the facts on what you need to know to make an informed analytics decision for your team without getting persuaded by chatter. Exploring Common Semantic Model Questions Fielded from Credit Unions and Community Banks Gemineye’s COO Matt Jefferson and Director of Business Development Maggie Chopp answer: Whether centralized analytics teams are better than departmental analysts Why unified semantic models ensure consistency If there should ever be a cap on how many fields your semantic model should have Why the misconception of limiting number of fields is holding back your analytics potential How credit unions are successfully integrating data from multiple sources without complexity  Full Transcript Alicia Disantis: Are centralized analytics teams better than departmental analysts? And Matt I will ask you that question. Matt Jefferson: I think centralized analytics teams there, they’re great for consistency and governance. Department analysts are great for kind of speed and the business contacts. I think the real win, no matter what you do in an organization, is when you really have them working with the same semantic model, the same governance principles. You know, I think at some level, you always end up with some level of decentralized, analytics. Maggie Chopp: We’ve seen a couple kind of creative configurations this year, and there’s nothing saying that they can’t work. Well, again, given the, the base semantic model like Matt mentioned, that’s kind of the key to making it all flow. But we have a number of credit unions we’ve talked to this year who have very interesting designs that work for their teams, depending on who rolls up to who. But it works nicely again, with the base model. We have a credit union that as an example that brought in data from their collections platform. A lot of that would never, ever hit the core. But it brought them a lot of value when it came to their repossessions. They ended up making a lot more money knowing which of their resellers were getting them the most margin. And again, that’s not something you’re going to find in a core provider. You’d really need a data warehouse to assess something like that. Alicia Disantis: Should there ever be a cap on how many fields your semantic model should have? Matt Jefferson: No, there should be an artificial cap. Right? Business questions evolve. You know, I always say that every financial institution is like a fingerprint, right? You know, it’s a financial institution, but it is a little bit different. Right? And the right solution, the technology solution, everything makes it very easy to adapt to your your business needs, right, to add the things you need. Do you really want to wait for someone to say like, hey, all of our clients need that particular data field before you can access it? Maggie Chopp: I’m gonna add a little caveat, which is it’s not always good to bring everything. And we want to, you know, optimize for efficiency. We want it to run quickly. Run well. But just from speaking from experience, the credit you and I worked at and all the credit runs we talked to, everybody needs something else. Like every single day. The models are great. We just need to add one more thing. And one thing that we’ve observed is that, some providers will make it really difficult to do that. You’ll have to go way back in time. There’s, a statement of work to draw up. There’s all this kind of finagling just to get one additional data field, which really, it shouldn’t be that complicated. We have a bring it all approach. And so bringing something like a single data field in is it is easy and it should be easy. And if it’s ever not, you should reassess how you’re doing it. And on top of that you know, the concept of even having placeholder fields is somewhat meaningful. We found pretty regularly talking to credit unions. They might all have a different definition of something. But if we keep placeholders in the code for different persona types or different segmentations that are credit unions already use, we don’t even have to go about adding a field, so to speak. We’re just using that credit unions logic. So no, we should never be limiting how many fields should be in a semantic model. Want More Data Analytics best practices? Check out our Data Teams page.

What to Do When Your Data Outgrows Excel

What to Do When Your Data Outgrows Excel

Microsoft Excel is where almost every financial institution’s data work begins. It is familiar, flexible, and already on every desktop. For years, it does the job. The monthly board report comes out of a spreadsheet. The branch numbers live in a spreadsheet. The incentive calculations, the loan summaries, the customer counts, all spreadsheets. And then, at some point, it stops working as well as it used to. The shift is rarely a single dramatic failure. It is a slow accumulation of friction: reports take longer, files get bigger and slower, one person becomes the only one who understands the master workbook, and small errors start having larger consequences. If that pattern feels familiar, your institution may have outgrown Excel without anyone formally deciding it was time to move on. The Signs Your Institution Has Outgrown Spreadsheets Outgrowing Excel looks different at every institution, but the warning signs are consistent. Reports that used to take an hour now take a day, because the data has to be pulled from more places and stitched together by hand. The same numbers do not match across departments, because everyone maintains their own version. A single analyst has become the keeper of a workbook so complex that nobody else can safely touch it, which means the institution has a key-person risk hiding inside a spreadsheet. In many cases it is not even an analyst doing the work but an executive, and a Chief Lending Officer who spends fourteen hours a month wrestling data inside a spreadsheet is fourteen executive-level hours not spent on strategy, lending growth, or leadership. Other signs are subtler. Decisions wait on data that is always a few weeks old by the time it is compiled. Nobody fully trusts the numbers, so meetings get spent debating whose figures are right instead of what to do about them. These are not signs of a team doing poor work. They are signs that the tool has reached the limit of what it was designed to do. Why Spreadsheets Become a Liability at Scale The core problem with relying on spreadsheets for institutional reporting is that they were never built for it. They were built for individual analysis, and they carry risks that grow as the stakes rise. Decades of academic research have reached a consistent conclusion: errors in operational spreadsheets are both common and serious, and they often go undetected until they have already affected a decision. When a spreadsheet drives a board report or a regulatory filing, an undetected error is not a small inconvenience. It is a real financial and compliance risk. Beyond errors, spreadsheets do not scale with the complexity of a modern financial institution. They cannot easily combine data from the core system, digital banking, loan origination, and third-party sources in a way that stays current. They have no built-in governance, so definitions drift and versions multiply. And they depend entirely on the people who build them, which means institutional knowledge walks out the door when those people leave. What was an asset at a small scale becomes a liability at an institutional one. What to Move Toward When Excel Is No Longer Enough Recognizing that you have outgrown Excel is the first step. The harder question for most executives is what to move toward, especially when the team has no appetite for a massive, disruptive technology project. The goal is not to abandon the kind of analysis the team does today. It is to give that analysis a stronger foundation. That foundation is a data platform that brings all the institution’s sources together automatically, maintains consistent and governed definitions, and delivers reporting that stays current without manual assembly. Instead of an analyst pulling exports and reconciling them by hand each month, the data flows in continuously and the reports update on their own. Instead of every department keeping its own version of the truth, everyone works from the same governed source. The analytical thinking the team already does remains. What changes is that the underlying work of gathering, cleaning, and reconciling data stops consuming all their time. Making the Transition Without Disruption The fear that keeps many credit unions on spreadsheets long after they have outgrown them is the fear of the transition itself. Leaders imagine a multi-year implementation, a steep learning curve, and a long period where nothing works. That fear is understandable, but it is based on an outdated picture of what moving to a modern data platform involves. The right platform is designed to integrate with the systems a credit union already runs, rather than requiring them to be replaced. Implementation is measured in weeks, not years. And because the goal is to make data easier to access, not harder, a well-designed platform reduces the learning curve rather than adding to it. The transition is not a leap into the unknown. It is a step onto firmer ground. Move Beyond Spreadsheets With Gemineye Gemineye’s Operations solution is built for credit unions and community banks that have outgrown spreadsheet-based reporting. Instead of waiting until month-end and assembling reports by hand, teams get detailed daily reporting from data that updates automatically. Manual work like branch incentive calculations, which can consume hundreds of hours a year in a spreadsheet, can be automated entirely. Because the platform unifies more than 75 data sources across core systems, digital banking, originations, and third-party vendors with consistent, governed definitions, your institution finally has one trustworthy source instead of a sprawl of workbooks. Implementation is measured in weeks, not years. If your data has outgrown Excel, see how Gemineye’s Operations solution gives your team a foundation built to scale with you.

Why Your Reporting Is Only as Good as Your Integration Layer

Why Your Reporting Is Only as Good as Your Integration Layer

Every credit union and community bank wants better reporting. Clearer dashboards, more reliable financial analytics, numbers leadership can act on without second-guessing. So institutions invest in reporting tools and dashboard platforms, expecting that better presentation will produce better insight. Often, it does not, and the reason is almost always the same: the problem was never the reporting layer. It was the data feeding it. A dashboard is only as accurate as the data behind it. A financial report is only as trustworthy as the systems it pulls from. When the underlying data is fragmented, inconsistent, or assembled by hand from sources that do not agree with each other, no reporting tool can fix that. The integration layer, the part of the stack that connects and reconciles your data, quietly determines the quality of everything built on top of it. Why Better Dashboards Do Not Fix Bad Data It is tempting to treat reporting problems as presentation problems. If the numbers are confusing or untrustworthy, the thinking goes, a better dashboard will help. But a dashboard does not create accuracy. It displays whatever it is given. If two source systems disagree on what counts as an active member, the dashboard will faithfully display a number that is wrong, just more attractively. McKinsey’s data teams have described this exact failure mode at financial institutions: leadership ends up debating the accuracy of the data instead of acting on the insights. When that happens, the reporting tool is not the problem. The data foundation is. This is why institutions that invest in executive dashboards or financial analytics without first addressing their integration layer are often disappointed. They have improved the window without fixing the view. The reporting looks more sophisticated, but the underlying trust problem remains, because the data still comes from disconnected systems that were never reconciled. What the Integration Layer Actually Does for Reporting The integration layer is the part of a data platform that pulls information from every source (the core system, loan origination, digital banking, the CRM, third-party vendors) and brings it together into a single, consistent foundation. It harmonizes definitions so a term means the same thing everywhere. It reconciles differences so the numbers agree. And it keeps the data current so reports reflect reality rather than a snapshot from three weeks ago. When this layer is working, reporting becomes trustworthy almost as a byproduct. A dashboard pulls from one reconciled source, so the numbers are consistent no matter who views them. A financial report reflects the same customer and account definitions as every other report, so the figures reconcile across departments. The reporting tools finally deliver on their promise, because they are working from data that can actually support them. How Both Data Teams and Executives Experience This Problem The integration gap shows up differently depending on where you sit, but it is the same root cause. For the data team, it appears as endless manual reconciliation: pulling exports, matching records, explaining why two reports disagree, and rebuilding the same logic every reporting cycle. The team knows the reporting is fragile because they are the ones holding it together by hand. For executives, it appears as a quieter erosion of confidence. The numbers in one report do not match another. A figure presented in a board meeting gets challenged and cannot be quickly defended. Over time, leadership learns to treat every number with a degree of skepticism, which slows decisions and undermines the entire purpose of having analytics in the first place. Both experiences trace back to the same place: data that was never properly connected before it reached the report. Why Data Lineage and Consistent Definitions Matter Two capabilities separate an integration layer that produces trustworthy reporting from one that does not. The first is consistent, governed definitions: the assurance that a field means the same thing everywhere it appears, so reports reconcile by design rather than by manual effort. The second is data lineage, the ability to trace exactly how a number moved from its source system through the platform and into a report. Lineage matters because trust requires verification. When an executive questions a figure, the data team should be able to show precisely where it came from and how it was calculated, rather than launching an investigation. When that traceability exists, confidence in reporting is durable, because it can be confirmed rather than merely asserted. Without it, every disputed number becomes a research project, and trust never fully takes hold. How Gemineye Builds Reporting You Can Trust Gemineye’s Data Integrations solution exists to give credit union and community bank reporting a foundation it can rely on. With more than 75 pre-built integrations connecting the core system, loan and mortgage origination, digital banking, CRM, and third-party data vendors, Gemineye brings every source into one reconciled environment. Consistent, customizable definitions mean a number means the same thing across every report, and end-to-end data lineage with a transparent data dictionary lets your team trace exactly how each field moves from source to dashboard. The result is that the dashboards and financial analytics your teams depend on are finally built on data they can trust. If your reporting is not as reliable as it should be, the place to start is the integration layer underneath it. See how Gemineye’s Data Integrations solution gives your reporting a foundation worth building on.

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