How to Use Machine Learning in Banking

Most banks know their data could be doing more for them, but insight decks and production systems often live in different worlds. Operational teams feel it first: underwriting queues that never quite shrink, fraud alert lists nobody can realistically clear, campaigns that look clever on slides but deliver modest results in the field.

Machine learning and banking initiatives address that disconnect by embedding models into day-to-day workflows, so the institution can approve, block, escalate or suggest with a level of consistency and speed that rules alone rarely achieve.

What is ML in banking

Machine learning in banking can be thought of as pattern recognition applied to financial decisions under strict constraints, increasingly embedded in modern financial software development services.

Instead of hand-written conditions like “reject if debt-to-income > 40%,” models are trained on years of history:

• millions of past loans, transactions and chargebacks;
• information on which applications defaulted, which alerts were confirmed as fraud, which customers churned or bought more.

From that, models learn which combinations of features tend to lead to favorable or unfavorable outcomes and emit a number the bank can act on: a default probability, a fraud score, a churn risk, a propensity to respond to an offer.

The machine learning algorithms in banking are familiar: gradient boosting, deep networks, sequence models, natural language processing, but the operating environment is unusually constrained:

• regulation (credit, privacy, anti-discrimination, capital);
• auditability (model risk management, explainability);
• risk appetite (a wrong answer can mean substantial losses or regulatory action).

These tensions are a recurring theme across AI and banking projects, not just ML models.

Use cases of ML in banking

Banks have something many sectors do not: large labeled datasets and repeated decisions and in the context of machine learning and banking (and modern banking software) that combination is unusually powerful.

A Citigroup analysis suggests machine learning in banking and related tools could eventually automate up to 80% of routine work, as a significant portion of that work is dealing with structured data.

Risk and credit decisions

Credit scoring remains a canonical example. Traditional scorecards look at a handful of variables (income, employment status, past delinquencies) and apply a simple formula. ML-based scorers:

• incorporate behavioral data such as account usage, bill payment patterns and spending volatility;
• capture interactions between factors instead of assuming each factor nudges risk independently.

The result is more approvals at the same portfolio risk, or lower loss rates at the same approval rate. Consumer lenders report double-digit increases in approval rates for thin-file applicants after switching from generic scores to boosted trees or neural models trained on richer data.

On the wholesale side, simulation models allow risk teams to explore a much wider range of stress scenarios. Instead of a small set of “severe but plausible” shocks, banks can test thousands of variations and see how defaults might cluster or propagate across counterparties.

Fraud detection and financial crime

How does machine learning help detect fraud in banking? It helps detect fraud by learning what “normal” looks like for each card, device or customer, taking into account geography, merchant type, time of day, device fingerprints and many other signals. Models then flag subtle anomalies and update as confirmed fraud and false alarms feed back into training.

The impact can be substantial. On Cyber Monday 2024, Visa reported its AI-driven fraud engine blocked 85% more suspected fraudulent transactions than the previous year. In anti-money-laundering (AML), similar techniques sift through transaction networks to surface unusual flows that a human analyst or rules engine would not identify.

Personalization and customer experience

For many customers, the primary interface to a bank is an app, not a person. Machine learning in banking turns those screens from static statement viewers into something closer to a personalized financial console and in some cases is combined with AR in banking to create richer, more interactive experiences.

Examples include:

• next-best action or offer: suggesting a savings product for someone with persistent surplus cash, flagging that a customer is ready for a credit limit increase, or nudging when a balance is trending toward overdraft;
• digital assistants: using NLP to answer questions (“What did I spend on travel last month?”), surface insights (“Utility bills are up 20% versus last winter”) and automate micro-savings or budgeting.

Process automation and operations

Bank operations rely heavily on documents, reconciliations and exception handling. Machine learning in banking is being deployed to take over many of the repetitive elements:

• extracting key fields from loan agreements, invoices and KYC forms;
• classifying and routing incoming tickets or emails to appropriate teams;
• spotting out-of-pattern entries in reconciliations and ledgers before they grow into larger issues.

At Citigroup, ML-driven developer tools and automated code reviews have created roughly 100,000 hours of weekly engineering capacity, including over one million automated code reviews in the first nine months of 2025. Applying similar multipliers in credit operations, compliance and finance quickly leads to large efficiency gains.

Benefits and challenges

Assuming that more models automatically yield a better bank would be optimistic. Gains are real, but so are the constraints that shape them.

Where ML helps

In broad terms, machine learning in banking delivers benefits in four areas:

• Cost and efficiency. Automating repetitive checks, reviews and routing allows staff to focus on judgment calls and relationships. Turnaround times fall; operational errors and rework decline.
• Revenue and growth. More accurate scoring and targeting mean saying “yes” more often to good customers and “not yet” to those who are not ready. Personalized offers and advice increase product penetration and wallet share.
• Risk control. Earlier, more accurate detection of bad loans, fraud and operational anomalies reduces losses and makes capital allocation less reliant on coarse averages.
• Customer experience. Faster approvals, fewer false fraud declines, more relevant nudges and smoother digital journeys encourage customers to stay and expand relationships.

What are the challenges of implementing machine learning in the financial sector?

Implementing machine learning in finance is difficult mainly because of strict regulation, messy data, legacy systems and a scarcity of people who understand both banking and ML. These are not new themes, but they manifest in particular ways when machine learning and banking intersect.

• Regulation, explainability and fairness. Models that affect credit, pricing, fraud or capital must be explainable, validated and tested for bias; opaque “black boxes” rarely pass model risk and supervisory review.
• Data quality and plumbing. Core systems were built for recording transactions, not serving ML features, so banks need cleaner, unified views of customers and accounts, consistent feature pipelines and enforced rules for PII and consent.
• Legacy integration and skills. Scores that arrive too late or cannot connect to dialers, CRMs or core systems add no value and there is a limited pool of people who can bridge Basel and hyperparameters, making cross-functional teams and MLOps discipline essential.

How to implement ML in banking?

There is no universal flow, but institutions that make machine learning for banking industry operations part of normal practice tend to follow a similar path.

Start from specific problems

The most effective programs start from concrete business objectives, not from a desire to “do ML.” Typical goals include reducing false fraud alerts while maintaining or improving detection rates, improving approval rates for a product without increasing losses, or shortening onboarding times for priority customer segments.

These objectives are defined jointly by business, risk, compliance and technology, with clear baselines and ways to measure change. Supervisors are often involved early and informally, so that intent and guardrails are understood before any model moves toward production.

Build a data foundation that can support capital decisions

Before models, there are unglamorous questions:

• which systems hold the authoritative records of customers, accounts, products and events?
• how can feature stores and pipelines be designed so calculations match between training and serving?
• where and how will consent, residency and retention rules be enforced?

This work is costly and slow, but it determines whether ML initiatives can scale or remain fragile proofs of concept.

Pick focused, visible use cases

Machine learning in banking earns trust through successful deployments in contexts that matter. Early candidates are typically:

• narrow in scope and straightforward to evaluate;
• impactful but not systemically critical (for example, fraud queues, document triage, internal support), before moving to core capital or pricing;
• close to existing workflows so staff can compare “before” and “after.”

An example is re-ranking existing fraud alerts by predicted risk so investigators can address the most promising cases first, then measuring hit rates and workload. Another is adding a conversational layer on top of an internal knowledge base and tracking ticket deflection.

Design models, MLOps and governance together

Training a model in isolation is the easy step. Moving it into production and keeping it there safely requires:

• repeatable training and deployment pipelines;
• integration with transaction systems, case management tools and alerting;
• model risk processes: documentation, independent validation, challenger models, re-approval cycles.

Treating ML components like any other critical system, with change control, monitoring and incident response, avoids both “shadow models” and brittle one-off integrations.

Plan for change and scale

Customer behavior shifts, new regulations arrive, new model families appear. Implementation questions are less about how to make updating and extending it routine.

That implies:

• regular retraining or recalibration where appropriate;
• a roadmap from initial use cases into adjacent areas once foundations are proven;
• training for frontline staff so they understand what the models are doing and where human judgment remains essential.

In the end, machine learning in banking changes less what banks do than how consistently and quickly they can do it. Those that build ML into everyday decisions and governance, rather than treating it as a one-off purchase, are the ones that usually see the biggest and most durable improvements in performance.