Financial institutions are rapidly turning to artificial intelligence to reshape how banking works.

AIs are not only there to help banks detect fraud, in which 90% of banks are said to be doing so, but they are also used in lending, where machine learning models are said to have helped improve default prediction accuracy by 15% to 25%, all while reducing loan losses by as much as 30%.

That could be why the total spending on AI within financial services has already surpassed US$35 billion, and is now on the course to reach US$97 billion by 2027, according to a whitepaper from Oradian.

For many institutions, the opportunity is difficult to ignore.

Machine learning models can sift through massive volumes of customer data, detect unusual transaction patterns in real time and uncover signals traditional analytics might miss.

Yet, enthusiasm and big spending do not necessarily guarantee results, as Oradian’s findings argue that the biggest obstacle to AI adoption is not technology.

Banks have frequently struggled to deploy AI effectively because the data required to train and run these systems remains fragmented across core banking platforms and operational systems.

This is true as algorithms alone are not enough. Successful AI initiatives rely heavily on strong data infrastructure.

Banks must make sure that they are able to access production-grade data, analyse it safely outside live core systems and move information through structured pipelines that connect raw data to automated decisions.

Without those foundations, even ambitious AI initiatives risk falling short.

Ambitious AI Plans Often Run Into the Same Problem

Every AI system depends on data. In many banks, however, valuable information sits scattered across different systems or locked within legacy infrastructure that was never designed for modern analytics.

Fragmented data leads to fragmented insights. When customer behaviour, transactions and operational information cannot be unified, machine learning models struggle to perform reliably.

The whitepaper argues that AI readiness is not simply a technology upgrade. It requires operational discipline across core systems, governance and organisational processes.

Institutions that succeed in deploying AI typically ensure that financial data is reconciled across channels, audit trails are maintained and analytics teams can access information without disrupting daily banking operations.

In practice, this often begins with building a reliable data layer.

You Have To Start With A Strong Data Infrastructure

Many institutions begin by creating a secure replica of their core banking database for analytics and experimentation.

This replica mirrors production data in near real time while remaining isolated from live banking systems. As a result, machine learning training and large-scale analytics can take place without affecting system performance for customers.

With full data available in a separate environment, banks can connect business intelligence tools, machine learning platforms and data warehouses more easily.

Transaction histories, behavioural logs and operational data become accessible for deeper analysis.

From there, financial institutions can build data pipelines that consolidate information from mobile apps, payment channels and core banking platforms into central repositories.

Standardising data definitions, removing duplicates and aligning customer identifiers across systems ensures analytics models receive consistent inputs. Governance rules determine which teams can access specific datasets, balancing innovation with security and privacy requirements.

While rarely visible to customers, this work often determines whether AI projects reach production or stall during development.

Once the groundwork is in place, banks can begin applying AI where it already delivers measurable results.

Where AI Is Already Delivering Results

Digital banking markets with high mobile adoption and large underserved populations offer particularly strong opportunities for AI-driven innovation.

The whitepaper highlights countries such as the Philippines, where growing digital ecosystems and limited traditional credit histories create demand for alternative approaches to financial services.

One of the most significant applications lies in credit risk assessment.

Smarter Credit Decisions Through Alternative Data

Millions of individuals and small businesses in emerging markets lack formal credit records.

Traditional scoring models often struggle to assess these borrowers. Machine learning models, however, can analyse a broader range of data sources.

Telecom usage patterns, mobile wallet transactions, utility payments, e-commerce activity and other behavioural signals can help lenders build a more complete picture of financial reliability.

Patterns such as consistent bill payments or stable mobile usage can reveal positive credit behaviour even when formal credit histories are limited.

According to research cited in the whitepaper, AI-driven models can improve default prediction accuracy by 15% to 25% compared with traditional methods while reducing loan losses by up to 30%.

Beyond improving portfolio performance, this approach expands financial inclusion by allowing entrepreneurs and gig workers to demonstrate their creditworthiness.

Fighting Fraud with Machine Learning

Fraud detection has become one of the most mature applications of AI in banking.

As digital transactions grow, financial crime becomes increasingly complex. Machine learning systems help banks identify suspicious patterns by analysing large datasets that include transaction activity, device fingerprints and behavioural signals.

Around 90% of financial institutions now rely on AI for fraud detection, according to the whitepaper.

AI-driven systems can achieve accuracy rates between 90% and 99%, significantly higher than the 35% to 70% accuracy typically associated with traditional rule-based models.

These systems identify anomalies that would be difficult for human teams to detect, such as unusual transaction timing or sudden changes in device behaviour.

As models learn from new data, their ability to distinguish legitimate transactions from fraudulent ones continues to improve.

Automating Service and Personalising Experiences

Customer service is another area where AI is reshaping digital banking.

Chatbots and conversational assistants can handle routine account inquiries, guide customers through onboarding and resolve simple issues at any time of day.

Automation helps banks scale support operations while allowing human teams to focus on more complex cases.

At the same time, AI-driven analytics enables banks to personalise services by analysing spending patterns and product usage.

Customers can receive tailored recommendations, relevant lending offers and insights that help them manage their finances more effectively.

Responsible AI Is Becoming a Banking Priority

Because financial institutions operate in heavily regulated environments, the use of AI requires careful oversight.

Models used for lending decisions, fraud detection or customer recommendations must remain transparent and explainable. Banks need to understand how decisions are made and ensure models do not introduce unintended bias.

Training data can contain hidden socio-economic patterns that influence outcomes. Continuous monitoring helps identify such risks and ensures compliance with regulatory expectations.

Responsible AI governance also requires maintaining clear audit trails and documentation so decisions can be explained to regulators and customers.

Banks That Get Data Right Will Lead the AI Era

Artificial intelligence is already transforming many aspects of financial services. Faster lending decisions, stronger fraud detection and more personalised customer experiences demonstrate its potential.

Yet the whitepaper returns to a simple conclusion.

Success with AI does not begin with algorithms. It begins with data.

Banks that invest in clean, accessible and well-governed data infrastructure will be far better positioned to deploy machine learning across their operations.

Those that overlook the groundwork may find that even powerful AI tools struggle to deliver meaningful results.

In the race toward AI-driven banking, data readiness is likely to become the real competitive advantage.

Much deeper insights can be found in The Digital-First Bank’s Guide to AI in 2026, available from Oradian.

Click on the image below to download the whitepaper.

Featured image: Edited by Fintech News Philippines based on an image by Freepik.