AI in Financial Services

AI in financial services isn’t new. Banks, insurers, and capital markets firms have been using machine learning for years to detect fraud, assess risk, and automate workflows.

What’s changed is how much more broadly and quickly it can now be applied.

With the rise of large language models, AI is no longer limited to structured data and narrow use cases. It can now interpret documents, support decision-making, and interact directly with both employees and customers. That shift is accelerating adoption across the industry.

Today, financial services sits near the top of all industries in both AI investment and expected impact. Roughly 35% of work can be fully automated, with another 35% significantly augmented.

But like most AI initiatives, the challenge isn’t recognizing the potential. It’s understanding where that value actually shows up and how to capture it in practice.

In a recent webinar, Dr. Tim Oates, Co-founder and Chief Data Scientist at Synaptiq, explored where AI is delivering the most impact in financial services today, and what it takes to implement it effectively.

Why Financial Services Is a Natural Fit for AI

Not every industry is equally positioned to benefit from AI. Financial services stands out for a few key reasons.

1. First, it is highly data-driven. Transactions, customer records, risk profiles, and market activity generate massive amounts of structured data.

2. Second, it is document-heavy. From loan applications to regulatory filings, much of the work involves processing unstructured text, an area where large language models perform particularly well.

3. Third, many workflows are repeatable and high-volume. This makes them ideal for automation or augmentation, especially when combined with human oversight.

The result is a unique combination: structured data for traditional machine learning, and unstructured data for language models, both operating within processes that are measurable and scalable.

That’s why AI is already creating value across multiple parts of the financial services value chain.

From Transactions to Patterns: How AI Is Transforming Fraud Detection

Fraud detection has long been one of the most mature AI use cases. But the way it’s being approached is evolving.

Traditionally, systems focused on individual transactions—flagging activity that appeared anomalous. Today, the focus is shifting toward relationships.

Financial activity can be modeled as a network, or graph, connecting accounts, devices, email addresses, and transactions. AI models can analyze these connections to identify patterns that would otherwise go unnoticed.

This enables institutions to:

• Detect coordinated fraud rings

• Identify hidden relationships between accounts

• Predict suspicious connections that are not directly observable

These approaches rely on techniques like graph neural networks, which are designed to operate on interconnected data rather than isolated records.

At the same time, identity verification is becoming more sophisticated. AI is being used to:

• Confirm identity through facial recognition

• Detect “liveness” to prevent spoofing with static images

• Validate secure IDs by analyzing features like holograms

• Even estimate physiological signals, like heart rate, from video to confirm a real human presence

But even with better detection, scale remains a challenge. Financial institutions process enormous volumes of transactions. Small error rates can produce overwhelming numbers of alerts. To manage this, AI is increasingly used to rank and prioritize cases, ensuring human investigators focus on the highest-risk activity.

And as fraudsters adopt AI themselves, this becomes an ongoing arms race rather than a solved problem.

Underwriting: Turning Documents Into Decisions

Underwriting is another area where AI is reshaping how work gets done.

The process is inherently document-heavy, requiring teams to review applications, financial statements, and supporting records. This creates friction: manual effort, data entry errors, and slow turnaround times.
Large language models address this directly.

They can extract, organize, and summarize information across documents, reducing the need for manual processing. This improves both speed and consistency, especially in high-volume environments.

On the decision side, machine learning models help assess risk by learning from historical outcomes, such as whether loans were repaid.

But this introduces its own complexity. Most applicants are creditworthy, which means models must be carefully designed to handle imbalanced data and avoid biased predictions.

Importantly, AI does not replace the underwriter.

Instead, it supports decision-making by highlighting risk factors, surfacing relevant information, and improving efficiency. Final judgment remains with humans, particularly in high-stakes or regulated contexts.

Customer Service: A Clear Opportunity for Measurable Impact

Few areas in financial services are as visibly broken as customer service

Long wait times, inconsistent communication, and repeated information requests create friction for both customers and employees. These issues are widespread and measurable.

That’s what makes customer service a strong entry point for AI.

AI systems can act as real-time assistants, helping agents by:

• Surfacing customer history and relevant documentation

• Suggesting next steps for common requests

• Providing consistent responses across channels

Behind the scenes, they can also summarize prior interactions, streamline workflows, and reduce manual effort. These improvements lead to faster response times and more consistent service, without removing humans from the process.

Just as importantly, the impact is easy to track. Metrics like wait time, resolution speed, and customer satisfaction provide clear signals of success.

Compliance: Managing Complexity at Scale

Compliance is one of the most complex functions in financial services, and one of the most resource-intensive.

Teams must monitor activity, investigate alerts, interpret regulations, and respond to audits. Much of this work involves reviewing large volumes of information under tight time constraints.

AI helps by reducing the burden of manual review.

It can:

• Triage and prioritize alerts based on risk

• Surface relevant data for investigations

• Summarize lengthy documents

• Generate draft responses for audits and regulatory requests

For example, transaction monitoring teams can focus on the highest-priority alerts instead of working through queues sequentially. KYC processes can be accelerated through automated document analysis. Compliance teams can quickly locate relevant policies and supporting evidence.

The result is not full automation, but faster, more consistent execution of complex tasks.

The Tradeoffs: Risk, Cost, and Control

As AI adoption grows, so do the risks.

Some are technical. Large language models can generate incorrect or unsupported information. Their decision-making processes are often difficult to explain, which can create challenges in regulated environments.

Others are operational. AI systems can be costly to run, particularly as usage scales. Many organizations also face vendor concentration, where reliance on a small number of providers introduces risk.

There are also broader systemic concerns, including cybersecurity threats and the potential for market concentration if many institutions rely on similar models and strategies.

Managing these risks requires strong governance, careful system design, and continued human oversight.

Getting Started: Why Focus Beats Transformation

One of the most consistent failure patterns in AI adoption is overreach.

Organizations attempt large-scale transformations before proving value. Or they delay initiatives while trying to fully clean and centralize their data.

A more effective approach is to start small.

Focus on a single workflow. Define a clear, measurable KPI tied to business value. For example:

• Reducing call center wait times

• Increasing fraud detection rates

• Improving processing speed for applications 

Build a pilot around that use case, keeping a human in the loop to validate outputs and monitor quality.

Measure outcomes carefully, not just speed, but also accuracy and downstream effects.

Then, once value is demonstrated, scale the solution into production.

The gap between a successful pilot and a production system is often where initiatives stall. Closing that gap requires sustained investment, executive support, and operational discipline.

From Potential to Practice

AI has clear potential in financial services. The technology is advancing quickly, and the use cases are well understood.

But value doesn’t come from the technology itself. It comes from how it’s applied.

The most successful organizations are not those pursuing broad transformation from the start. They are the ones identifying specific problems, applying AI in targeted ways, and measuring the results.

Over time, those focused efforts compound, turning isolated wins into scalable capabilities.

That’s how AI moves from experimentation to infrastructure.

And that’s where it starts to deliver real, lasting impact.

Looking to apply AI in financial services but not sure where to start?

Synaptiq can help you identify high-value use cases, launch focused pilots, and define clear KPIs so you can measure results, demonstrate ROI, and confidently scale what works.

Reach out here to learn more or discuss your next project.