- Practical insights, numbers, and real-world cases
Data Analytics in Banking:
10 Practical Use Cases
Where is your financial institution losing money, which risks can be spotted early, and what already delivers measurable results? ​​We analyze the experience of Cobit Solutions and international banks.
2–3×
higher engagement with personalized offers
18%
higher customer retention
rate
15–20%
lower risk of problem
loans
67.6%
of loss events prevented
Up to 3×
more AML schemes
detected
How Does Analytics Pay Off for a Bank?
These are the metrics bank owners and executives use to judge data initiatives.
More sales per customer
Fewer credit losses
Accurate capital planning
Lower operating costs
In this article, we will analyze 10 practical scenarios for using analytics in banks.
You will see:
- which solutions international banks already use
- what results they achieve
- what data and infrastructure are needed to run such scenarios
- where analytics can have the greatest business impact
In this article, we draw on Cobit Solutions’ experience and publicly available cases from international banks. Our banking clients are covered by NDAs, so we don’t name them.
Let’s start with the question that comes first for any business.
OPERATIONAL EFFICIENCY
How to reduce bank operating costs
Now let’s move on to Cost-to-Income. Where does the bank spend money every day? Cash, collection, employee labor, maintenance of a large network of equipment.
On the scale of a large financial institution, even a few percent savings turn into significant amounts.
Cash flow optimization
Generative AI assistant
Predicting ATM failures
01
Real-time hyper-personalization
Let’s imagine a client who has just purchased airline tickets. For the bank, this is a signal: in the near future he may need insurance, better terms for payments abroad or another travel product.
There can be hundreds of such signals: an increase in the balance, a regular payment, a change in the cost structure, a new large transfer. Predictive analytics helps the bank analyze these signals and determine which product is most likely to interest a particular client.
In our experience, personalized offers can attract the attention of customers 2–3 times more often.
Personalized offers can be 2–3 times more likely to attract customer attention.
02
Predicting customer churn
When should a bank act on a customer who may leave? Long before the account is closed, because warning signs appear much earlier. The client uses the card less often, moves recurring payments to another bank, keeps lower balances, and moves less money through the account.
Predictive analytics helps to combine these changes and identify clients with a high risk of churn. In one of our cases, a European bank used machine learning to predict the churn of banking customers. A targeted campaign helped increase the level of active client retention by 18%.
Higher customer retention.
03
New sources of revenue from data
Can data help a bank find additional sources of revenue? Yes. Aggregated analytics allows you to study consumer activity, payment patterns, segment behavior, and changes in demand. Based on such findings, a bank can find new opportunities in existing products or create separate analytical services for corporate clients and partners.
In one of the cases Visa a large American card issuer used analytics to identify lost opportunities for generating commission income. After the changes, the portfolio’s return increased by 3%, and the bank began to receive an additional $100,000 per year.
Portfolio
returns increased
Additional income per year
Key findings
How does data turn into revenue?
Signal
The customer changes behavior, balances, payments, or activity.
Forecast
The model identifies a need, risk, or potential opportunity.
Action
The bank launches a personal offer, contact, or other scenario.
Effect
The bank increases sales, retains customers, and finds new sources of income.
Want to earn more from data?
We offer a 30-minute consultation to identify the most promising scenarios for your income growth.
Or text us via Messenger
CAPITAL PROTECTION
How does analytics protect bank capital?
The next question: where could the bank lose money? Here, analytics works on several levels at once. It helps evaluate borrowers, monitor the loan portfolio, detect fraud, and model capital loads.
04
Next-generation credit scoring
Two clients may have similar credit ratings and completely different levels of risk. For a more accurate assessment, the bank can use predictive analytics built on machine learning models. Such models take into account cash flow, transaction behavior, business characteristics, and other available factors.
Based on this data, it is possible to more accurately assess the probability of loan default. The results of the analysis help when making a decision, determining the limit and rate, and further monitoring the borrower.
In our experience, using ML models helps reduce the risk of problem loans by 15–20%.
15-20% lower risk of non-performing loans (NPLs)
05
Early detection of problem loans
The loan has been granted. What happens to the risk next? After all, the borrower’s financial condition can change. A drop in turnover, a decrease in revenues, an increase in debt burden, and a change in payment behavior can indicate a future problem.
The early warning system based on predictive analytics collects such signals and forms a list of clients that need attention.Â
So the risk team gets the opportunity to work with them earlier and set priorities correctly.
In World Bank 2025 study, ML helped prevent 67.6% of incidents that could have led to financial losses.
67.6% of financial losses were prevented
06
Intelligent Antifraud and AML
A single suspicious transaction may reveal nothing. But what if you look at the entire chain?Â
Machine learning and network analysis help find connections between accounts, payments, and participants that remain invisible when checking individual transactions.
A case in point is the international project Aurora of the Bank for International Settlements.
During testing, the combination of machine learning, network analysis, and data protection technologies allowed detectingup to three times more complex money laundering schemes.
The number of false positives was reduced by 80%.
up to 3× more complex AML schemes · 80% fewer false positives
07
Stress Testing and Capital Planning
What will happen to a bank’s portfolio during a deep recession? Scenario modeling helps answer this question. A bank can estimate potential losses, revenues, expenses, and capital levels under different economic conditions.
In 2026, the Federal Reserve examined 32 large banks, including Bank of America, JPMorgan Chase, Citigroup, Goldman Sachs, Capital One, and Wells Fargo.
Under a deep global recession scenario, cumulative losses could exceed $708 billion, and the Tier 1 capital ratio would decline by 1.6 percentage points.Â
At the same time, all 32 banks maintained capital above minimum regulatory requirements and were able to continue lending.
$708 billion of potential losses identified for just part of 2026
In addition to regulatory reviews, banks can conduct their own internal stress tests for specific risks and management tasks. For example, they can simulate a sharp increase in delinquencies, a change in interest rates, or a deterioration in a specific loan segment. Such calculations help to assess the capital buffer in advance, adjust credit policy, and prepare for possible changes in the market.
Where could your financial institution be losing money due to data?
We offer a 30-minute consultation to identify the most promising scenarios for your income growth.
Or text us via Messenger
OPERATIONAL EFFICIENCY
How to reduce bank operating costs
Now let’s move on to Cost-to-Income. Where does the bank spend money every day? Cash, collection, employee labor, maintenance of a large network of equipment.
On the scale of a large financial institution, even savings of a few percent add up to significant sums.
Cash flow optimization
Generative AI as an assistant
Predicting ATM failures
08
Cash handling optimization
How much cash does a particular ATM need tomorrow? It can be forecast. The model takes into account transaction history, day of the week, seasonality, holidays, location, and other factors.Â
Based on the forecast, the bank plans ATM replenishment and collection routes.
Cobit Solutions estimates that cash handling can account for 5–9% of a bank’s total operating costs. Yet nearly half of our clients still calculate manually how much cash will be needed.
of the bank's operating expenses may be related to cash handling
09
AI assistant for bank employees
How much time does a bank employee spend searching for a document, internal policy, or necessary instruction? A corporate AI assistant can find information, summarize documents, and help prepare for a meeting.
Such solutions are already being used in real banking processes.
One of the most striking examples is Bank of America. Erica for Employees, the bank’s internal assistant, is used by more than 90% of staff, and the number of calls to IT support has decreased by more than 50%.
employees use Erica, and the number of support requests has decreased by 50%
10
Predicting ATM Failures
ATM failures can also be predicted. The equipment constantly generates event logs, error messages, and technical indicators. Machine learning models analyze this data and find signs that may precede the failure of individual components.
This approach was used by Bank of New Zealand. The predictive maintenance system helped the bank cut ATM downtime by a third, errors by 65%, and maintenance costs by 30%.
lower maintenance costs
less ATM downtime
Key findings
Where does the bank save money every day?
Cash and collection
more accurate replenishment forecasts and better routes
Employee time
faster access to documents and instructions
Equipment
there is significantly less downtime and fewer emergency repairs
Processes
lower operating costs and stable service
What processes in your bank take up the most resources?
Let's discuss in a short consultation where predictive analytics, automation, or AI can reduce costs and simplify daily work.
Or text us via Messenger
Why Is a Good Use Case Not Enough?
Where does the data live? Partly in the core banking system (CBS), and partly in CRM, card processing, mobile banking, and credit systems.
Metrics can have different formats, calculation rules, and update frequencies. Combining these sources is the hardest part of any analytics project.
Data silos
Most of the scenarios described require information from multiple systems at once. Therefore, the first task is to integrate the sources and create a common data environment.
Data
quality
The model inherits the problems of the original data. Duplicates, gaps, different calculation rules, and errors directly affect the result.
Legacy systems
New solutions often integrate with systems that have been operating for decades. The architecture must ensure stable data exchange and the ability to connect new scenarios.
Skills gap
A full-fledged analytics system requires architects, data engineers, BI specialists, analysts, and machine learning specialists. The team must simultaneously understand the technology and the bank's business objectives.
Security and compliance
Access to banking data requires clear rules. Roles, logging, privacy, model control, and regulatory compliance are built into the project architecture.
As a result, even a single use case can quickly affect multiple systems,
data sources, and processes across the bank.
Technology foundation
How Cobit Solutions helps banks work with data
All 10 scenarios we discussed above require quality data, the right architecture, and analytical models.
Cobit Solutions can close this technology loop — from the unification of banking systems to predictive analytics and AI-based solutions.
Hyperpersonalization
We combine transactional and customer data and prepare it for predictive analytics. We build machine learning models for personalized offers.
Forecasting churn
We build a predictive model that identifies customers with an increased risk of churn. It help form target groups for retention.
New sources of revenue from data
We create a single repository and prepare aggregated datasets. We build analytical products for internal teams, corporate clients or partners.
Credit scoring
We develop machine learning models for credit risk assessment. We integrate the results into the bank's analytical system.
Early detection of problem loans
We set up predictive models and an early warning system that help identify borrowers with increased risk.
Antifraud and AML
We combine transaction flows and prepare data for anomaly detection. We build models to search for suspicious transactions and patterns.
Stress testing and capital planning
We build scenario models for different economic conditions. They show the possible impact on the loan portfolio, profit and capital.
Cash flow optimization
We create predictive models to plan the cash needs of ATMs and branches. We visualize the results in Power BI.
AI assistants for employees
We prepare corporate data for working with large language models. We create internal AI solutions for searching, analyzing, and processing information.
Predicting ATM failures
We build predictive maintenance models. We use event logs, fault history, and equipment performance for analysis.
To implement such solutions, the Cobit Solutions team works with data integration, ETL/ELT, DWH, Data Lake and Lakehouse, Power BI, predictive analytics, machine learning, and AI. We can separately build a data management system, configure quality control, access, and prepare a single analytical framework for multiple scenarios.
Which use case should your bank start with?
Book a free 30-minute consultation to find the most promising revenue use cases for your bank.
Or text us via Messenger
What analytics in Power BI might look like
Due to data confidentiality, we do not publish actual dashboards. Below are a few examples that demonstrate the capabilities of Power BI and Cobit Solutions' approach to business data visualization.
We also offer you to familiarize yourself with our other projects
Healthcare
“Dobrobut” clinics network
Result:
- The clinic’s directors receive a P&L report updated daily.
- Management uses a daily dashboard for budget-versus-actual analysis of operational indicators.
- Dashboards combine data from ERP and the Doktor Eleks medical system.
Logistics
Meest China
Result:
- Dashboards combine data from ERP and CRM.
- Route margins are automatically calculated in dashboards and monitored every day by management.
- A Profit & Loss report is generated daily.
Manufacturing
Steelite International USA
Result:
- Automated reporting, reducing turnaround from 10 days to overnight.
- Scaled visibility from regional to global (US, UK, Canada, Europe, Australia).
- Centralized data enabled data-driven decisions on a weekly cadence instead of monthly.
Industry-focused case studies designed for your needs
