---
slug: "finance-banking"
name: "Finance & Banking"
tagline: "Credit risk · customer acquisition · valuation · macroeconomic forecasting"
description: "How tabular & time-series machine learning supports credit risk, bank marketing, property valuation, and macroeconomic forecasting with real benchmarks."
datasetIds: ["credit-g", "bank-marketing", "house-sales", "houses", "us-consumption-1y"]
canonicalUrl: "https://carlahq.eu/machine-learning/finance-banking"
---

# Machine Learning for Finance & Banking

> Credit risk · customer acquisition · valuation · macroeconomic forecasting

## Domain Overview
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. Multi-step forecasting models project future sequences, such as national consumption and disposable income over multi-year horizons. The evaluation split should also reflect how the model will encounter new customers or future periods.

## Business Decision Use Cases
| Decision Problem | Formulation & Operational Impact |
| :--- | :--- |
| **Credit risk** | Estimate the likelihood that an applicant will repay, then combine that estimate with exposure, affordability, policy rules, and human review. |
| **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 macroeconomic forecasting** | Estimate property values, asset balances, or project multi-year macroeconomic consumption and disposable income trajectories for financial planning. |

## Recommended Data Attributes
- 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
- Macroeconomic series, consumption indicators, and interest rate trends

## Evaluation Split & Benchmark Considerations
- 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.
- Time-series forecasting evaluates probabilistic calibration across quantiles (SQL) to capture upside and downside financial risk.

## Privacy, Governance & Regulatory Compliance
- 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.

## Representative Benchmark Datasets
- [credit-g](/datasets/credit-g.md) — Credit risk
- [bank-marketing](/datasets/bank-marketing.md) — Marketing response
- [house-sales](/datasets/house-sales.md) — Property valuation
- [houses](/datasets/houses.md) — Housing value estimation
- [us-consumption-1y](/datasets/us-consumption-1y.md) — Macroeconomic consumption & income forecasting

## Canonical Resource
- **HTML Guide:** [https://carlahq.eu/machine-learning/finance-banking](https://carlahq.eu/machine-learning/finance-banking)