AI lenders

Like in many industries, artificial intelligence (AI) is inundating the financial services sector with opportunities to streamline operations, accelerate growth, and better cater to customer needs.

Notably, one McKinsey & Company analysis found that AI could increase earnings for financial service providers by up to $340 billion through improved product productivity.

This is just the tip of the proverbial iceberg.

Specifically, AI is helping traditional financial institutions and upstart fintech firms solve one of their biggest challenges: Combating fraud without compromising the user experience.

Understandably, consumers expect digital interactions to be safe and place the burden on businesses to ensure security. To deliver, financial services providers are eagerly adopting AI, leveraging the technology to analyze data and report on risk efficiently.

It’s an enormous opportunity to improve banking for companies and their customers, but it’s not without real challenges that can’t be ignored.

Understanding the Risks of AI Implementation

AI in general and Large Language Models (LLMs) in particular are predicated on data, consuming expansive troves of online information and developing an incredible capacity for data analysis.

As a result, it can quickly analyze vast volumes of digital data to identify patterns of suspicious activity or potential fraud.

The problem: we don’t know how it’s doing that.

As Anthropic’s Sam Bowman told Vox, “We just don’t understand what’s going on here. We built it, we trained it, but we don’t know what it’s doing.”

The risk is real. Artificial intelligence isn’t actually “artificial.” These systems are human-made, flaws and all.

Explaining the risks, the Federal Trade Commission notes that “AI models are susceptible to bias, inaccuracies, ‘hallucinations’ and bad performance.”

This is fine if you’re using AI to create an acrostic poem about fraud prevention best practices, but it’s problematic if you’re leveraging the technology to make real-time decisions about people’s financial futures.

Simply put, relying exclusively on AI as a fraud prevention solution exposes businesses to complicated ethical, legal and regulatory concerns when explaining their decisions.

The potential for compliance departments to find themselves on the wrong side of a compliance audit or class action lawsuit is reason enough to ensure that automation solutions are supervised and explicable.

In August 2023, the US Consumer Financial Protection Bureau issued guidance for credit decisions, requiring lenders to provide specific and accurate reasons for credit decisions.

Of course, the moral and ethical implications are similarly critical, ensuring everyone has fair access to the best platforms and services.

To maximize AI’s potential while mitigating potential weaknesses, companies should leverage a balanced, transparent approach to AI implementation, deploying a rule-based system combined with human intelligence.

How to Implement Human-Supervised AI

AI has unprecedented potential to help financial services companies identify and prevent fraud. It also has inherent risks that can’t be ignored. However, it can become invaluable when implemented with human supervision and intelligent verification technology.

Here’s how to leverage human-supervised AI to enhance fraud detection and prevention, especially during customer onboarding.

#1 Know Your Customer (KYC) Compliance

KYC requirements are critical to fintech’s efficacy and compliance readiness, but it can also be a time-consuming process that can erode the customer experience.

Human-supervised AI can streamline the process by eliminating the need to manually verify an ID and provide the explanation needed for compliance and regulatory requirements.

Human intervention brings the potential to correct for bias that may exist in a data set. In essence, the strengths of expert human analysts in identifying AI’s blind spot can mitigate its risks.

#2 Enable a Multi-Layered Ecosystem

A single point of failure is a recipe for fraud prevention disaster, exposing companies to ineffective fraud detection practices, compliance concerns, and other challenges.

In contrast, a multi-layered ecosystem makes fraud detection and prevention an agile process with far-reaching implications for user experience and platform efficacy.

For instance, a fraud analyst can step in if an AI system rejects a legitimate ID, determine how the error occurred, and teach the computer how to spot similar issues in the future.

This continuous feedback improves machine learning models through constant input and refinement.

On the other hand, an AI system without oversight will assume uncorrected bad behavior is accurate and will continue making the same decisions, thereby exacerbating the problem.

#3 Adapt to Evolving Fraud Trends

Fraud detection is a moving target as bad actors continually adjust their tactics in response to the latest prevention methods.

For many fintech and financial service providers, fraud detection is a high-stakes game of whack-a-mole, and any potential advantage is a welcome development.

AI alone won’t accelerate future fraud detection because it analyzes data patterns and assumes future activity will follow those same patterns.

Meanwhile, a trained fraud analyst will catch novel threats AI systems miss and, with continuous feedback, enable the learning model to improve through constant input and data refinement.

AI-Powered Improvement

Fostering convenient and secure experiences requires a balance of fraud prevention and friction management. Organizations can deliver a user-friendly, low-friction experience by keeping identity verification and fraud processes in the background.

This process leverages key advantages of AI, but superior identity verification still requires human expertise.

In this way, AI’s incredible capabilities go hand-in-hand with human experience and expertise, creating a fraud identification and prevention mechanism that protects financial service providers and their customers.

 

About the Author: Crystal Blythe is the vice president of customer success and fraud at IDology, a GBG company and industry leader in identity verification, AML/KYC compliance, and fraud management solutions that help businesses establish trust, drive revenue, and deter fraud.