Credit risk
Estimate the likelihood that an applicant will repay, then combine that estimate with exposure, affordability, policy rules, and human review.
MACHINE LEARNING USE CASES
Machine learning for finance & banking
Credit risk · customer acquisition · valuation · forecasting
Machine learning in finance improves recurring decisions. Which applications need review? Which customers may respond to an offer? What value or amount should a team expect? The answers often sit in customer, account, transaction, and asset tables.
Classification models estimate a category, such as likely default or campaign response. Regression models estimate a number, such as property value or expected balance. Both need a defined decision and an observable target. The evaluation split should also reflect how the model will encounter new customers or future periods.
REAL DATA, REAL RESULTS
Finance & banking datasets and benchmarks
Each row connects a business problem to its dataset profile, held-out benchmark results, and prepared Google Sheet. Dataset size provides context. It isn't a quality score. Compare results only when the tasks and metrics match.
| Dataset | Problem | Task | Rows | Features | Evidence | Carla |
|---|---|---|---|---|---|---|
| Credit G | Credit risk | Binary classification | 1,000 | 20 | Benchmark | Try Credit G in Google Sheets |
| Bank Marketing | Marketing response | Binary classification | 45,211 | 15 | Benchmark | Try Bank Marketing in Google Sheets |
| House Sales | Property valuation | Regression | 21,613 | 20 | Benchmark | Try House Sales in Google Sheets |
| Houses | Housing value estimation | Regression | 20,640 | 8 | Benchmark | Try Houses in Google Sheets |
FROM QUESTION TO PREDICTION
What can machine learning do in finance & banking?
A worthwhile use case has an outcome you can define, examples of past outcomes, inputs available at prediction time, and a workflow that can act on the result.
Customer acquisition
Rank customers by likely campaign response so teams can focus limited contact capacity and reduce unnecessary outreach.
Fraud and anomaly triage
Flag unusual combinations of transaction, device, merchant, or account attributes for investigation rather than treating a score as proof of fraud.
Customer retention
Identify accounts showing patterns associated with attrition early enough for a useful service or retention intervention.
Valuation and forecasting
Estimate continuous amounts such as property value, demand, revenue, loss, or balance using known attributes and historical outcomes.
INPUTS AND TARGETS
What kind of data is useful?
Start with fields that exist before the prediction is made. Remove identifiers unless they serve a defensible purpose, and watch for columns that reveal the outcome indirectly.
- Account tenure, product holdings, balances, payment history, and prior outcomes
- Application and affordability attributes captured before the decision
- Campaign channel, timing, contact history, and consent status
- Asset characteristics, location, comparable transactions, and observation date
READ THE EVIDENCE
What do the benchmarks tell you?
Carla’s benchmark pages use persisted held-out splits. Models train on one set of rows and run on another. This keeps comparisons reproducible across the browser and Python runners. It doesn't remove the differences between a public dataset and your process.
Use the benchmark to inspect task formulation, metrics, variability, and runtime. Then repeat the evaluation on your own historical data with a split that resembles future use.
- Credit and response datasets are commonly imbalanced, so accuracy alone can hide weak performance on the outcome that matters.
- False approvals and false declines have different costs. Threshold selection belongs to the business decision, not only to the model benchmark.
- Valuation is a regression problem: inspect absolute error and error distribution, not classification metrics or a single average score.
RESPONSIBLE USE
Privacy and decision safeguards
Financial tables can contain identity, income, transaction, and behavioral data. Minimize columns, remove direct identifiers, restrict access, and test whether proxy variables recreate protected or sensitive attributes.
A benchmark measures predictive behavior on one historical dataset. It doesn't establish regulatory compliance, fairness, or suitability for automated lending. High-impact decisions need governance, monitoring, explanations, and meaningful human oversight.
Before deployment, document the intended user, decision, target definition, acceptable errors, excluded uses, and a process for monitoring changes. Keep a manual path for unusual cases and for people affected by consequential decisions.
START WITH YOUR OWN TABLE
Try Carla Have your own finance & banking dataset?
Run a model for rows and columns directly on your data in Google Sheets. Carla runs it locally in your browser. Your spreadsheet doesn't need to go to a model server.
PRACTICAL QUESTIONS
Frequently asked questions
What is a good first machine learning project in Finance & Banking?
Choose a repeated decision with a measurable historical outcome, data available before the decision, and a person who can act on the prediction. Begin with a narrow classification or regression question rather than trying to automate an entire process.
Do I need to be a data scientist to test these examples?
No. The linked dataset pages explain the target and benchmark, and Carla lets you run a tabular foundation model from Google Sheets. Production decisions still require appropriate domain, data, and governance expertise.
Does benchmark performance guarantee results on my data?
No. A benchmark measures a model on a specific dataset and evaluation design. Your population, definitions, data quality, time period, and workflow can produce materially different results.