Loan Analytics for Credit Unions & Banks | Arkalytics

What is Loan Analytics?

Loan analytics is the result of combining loan account origination and servicing data with transactional data to measure asset quality; group loans into homogeneous pools to analyze performance and risk; identify trends and anomalies, including performance of related parties, such as indirect auto-dealers or correspondent mortgage originators; and optimize portfolio risk and return.

It has configurable dimensions, like loan types, balance ranges, credit score bands, interest rate bands, statuses, maturity ranges, vintage, and past-due banding that fit any portfolio strategy. Arkalytics delivers loan analytics capabilities through a governed data lakehouse built specifically for credit unions and community banks.

ArkaIQ - Ask a question.
Get an answer in seconds.

"What is our total loan balance by loan type and delinquincy status?"

ArkaIQ is an AI assistant built directly on top of your governed data foundation. Frontline staff, analysts, and leadership can ask natural language questions about members, loans, accounts, and trends to get accurate answers in a fraction of the time it takes to build a custom report.

Natural and plain language questions, answered from your data
No SQL necessary, no waiting on an analyst or a report request
Built on a secure, governed data lakehouse foundation

Loan analytics: What lending and risk teams ask us first.

Why is loan analytics important for credit unions and banks?

Lending is a long-term customer relationship. Loan analytics helps credit unions and banks understand not just how much they're lending at any point in time, but the health of the underling relationship that rolls-up into the risks and opportunities spread across dealers, geographies, loan types, and individual borrowers.

Without it, institutions are transacting and reacting to problems (like rising charge-offs or concentration risk) after they show up on a balance sheet, rather than building those relationships by spotting the patterns early.

How can I explore my loan portfolio without SQL or IT support?

Users can quickly leverage secure generative AI tools like ArkaIQ to ask questions of their institution's data for quick answers with all the details needed to confidently use it.

Once you're grounded, the Loan Workbench in Arkalytics lets you filter, slice, and drill into individual loans or groups of loans by variables such as loan type, source, status, balance, and credit score, without writing SQL or filing an IT request to create the ongoing reports needed to action the insights found. These are built for self-service exploration and action by your lending and analytics teams.

How can I analyze lending performance by branch, geography, and product?

Arkalytics lets you compare your loan portfolio by geography and branch, identify your most popular products, examine originations versus maturities, and evaluate dealer and indirect-lending trends, all at right now or at/over any point in time, all from the same unified data foundation.

How does Arkalytics standardize loan reporting across systems?

Arkalytics standardizes loan analytics across systems through a consistent data model, business rules, and configurable mappings which includes:

Normalizing loan data into a common banking data model
Translating source codes into standard business-friendly categories
Applying consistent hierarchies across dashboards and analytics
Applying self-service updates that keep mappings updated
Loan analytics across all connected data sources

How can we analyze our loan portfolio?

A practical approach to loan portfolio analysis centers around four key areas:

Tracking trends in core KPIs/KRIs (loans opened, payoffs, charge-offs, delinquencies, balances)
Segmenting the portfolio into homogeneous groupings by loan type/branch/geography, etc., as well as time/vintage and understanding the variations in trends across each area and over time
Creating thresholds and triggers on groupings that help you identify, measure, monitor, and control concentration risk (credit score, LTV, DTI, loan types, etc.)
Reviewing overall growth versus maturity, and drilling into details via tools like a loan workbench, to determine if extrapolations of existing KPIs/KRIs are reasonable

The overarching goal is to move beyond simply noting "what changed" to understanding "why it changed" and which products, segments, or sources explain the change, so that insights can inform decisions in risk management, product strategy, and lending.