Fraud analytics in banking: Mitigating risks with AI

MAY. 22, 2024

Art Crosby, Managing Director

Fraud in the banking sector is becoming more sophisticated as cybercriminals evolve their techniques.

With billions lost annually to various forms of fraud, financial institutions are increasingly relying on advanced fraud analytics in banking to stay ahead of these threats.

By leveraging AI-powered solutions, banks can detect anomalies and proactively mitigate risks, gaining a competitive edge in securing their assets and customer data. Understanding how fraud analytics functions in the context of banking is crucial for protecting against financial crime and ensuring compliance with regulatory standards.

Key takeaways

1. Fraud analytics in banking relies on advanced AI and machine learning to detect and prevent financial crimes in real-time.
2. Implementing a scalable data infrastructure and multi-layered security approach significantly enhances fraud detection capabilities.
3. Regular updates and training of AI models are essential for keeping up with the evolving tactics of fraudsters.
4. Collaboration across departments boosts the effectiveness of fraud prevention strategies.
5. Visual data tools and dashboards help streamline decision-making in fraud detection.

Unfortunately, banks are losing ground. A recent report from Nasdaq found that “the scale and pervasiveness of financial crime is immense…fraud losses totaled $485.6 billion [in 2023] from a range of devastating fraud scams and bank fraud schemes, worldwide.”

To combat this surge, banks are turning to AI-powered fraud analytics solutions. These tools proactively identify anomalies and high-risk areas, giving banks the upper hand in the ever-evolving fight against financial crime.

What is fraud analytics in banking?

Put simply, fraud analytics is the use of advanced data analysis tools and techniques to detect and prevent fraudulent activities within financial institutions. It leverages both historical and real-time data to identify, assess, and mitigate risks associated with fraud and data breaches.

Specific use cases for fraud analytics in banking

While each fraud scheme is unique, there are a few types that present the greatest threat to banks and their customers.

Here are some of the most common use cases for combating financial crimes via fraud analytics:

Account takeover prevention

In an account takeover (ATO) attack, a malicious actor gains unauthorized access to a user’s account, taking control to commit fraud, steal data, or engage in other harmful activities. It’s one of the most common types of fraud, with nearly one-third of people being victims of this attack.

By the time a bank registers that an ATO attack has taken place, it’s usually too late. Some funds may be recouped, but the bulk of the damage is already done.

That’s why proactive measures, such as AI-powered fraud analytics, are so valuable. They can detect suspicious login attempts, unusual activity patterns, and potential identity theft and stop an attack before it leads to significant financial and personal data losses.

"Banks are turning to AI-powered fraud analytics solutions to combat fraud at scale."

Anomaly detection in wire transfers

Wire transfers are a major risk for money laundering and other illicit activities. Fraud analytics tools can flag large or unusual wire transfers for closer scrutiny.

By examining the context and patterns of these transactions, banks can uncover and prevent illegal activities, ensuring adherence to global anti-money laundering (AML) standards and other banking compliance regulations.

Payment fraud detection

Fraud analytics is indispensable in rapidly growing areas like online banking and e-commerce, where physical verification of cardholders is impossible.

These tools can identify potentially fraudulent transactions in real time, allowing banks to intervene before the transaction is completed.

Tailor-made solutions are ideal

These are just a few of the most common types of financial fraud — there are dozens more.

Since no two schemes are exactly alike, the usefulness of out-of-the-box tools is limited. To effectively combat these ever-evolving threats, banks need a custom-built defense strategy.

Here’s a breakdown of the steps required for building a robust financial fraud analytics program.

The complex challenge of banking fraud

Fraud and data security breaches in banking are particularly insidious. Financial institutions have a mountain of data and transactions to keep track of, and spotting fraud can feel like finding a needle in a haystack.

What’s worse, these crimes tend to lack consistent patterns, making them even harder to detect. Whether it’s identity theft, cyberattacks, or money laundering, each scheme leaves well-camouflaged footprints disguised as legitimate transactions.

Think about a casino. They have entire teams solely dedicated to spotting unusual betting patterns and can typically root out cheating fairly quickly. But for banks, it’s a whole different ball game. You can have tons of tiny transactions that add up to a large money laundering operation, which is incredibly difficult to spot.

Humans alone are no match for modern fraud schemes — the sheer number of transactions is too much to handle. Plus, bank staff can add more value by spending their time on core business activities rather than sifting through thousands (or millions) of transactions.

Instead, banks are turning to AI-powered fraud analytics solutions to combat fraud at scale.

Key elements of fraud analytics in banking

Fraud analytics in banking involves using advanced data analysis techniques to identify patterns indicative of fraudulent activity. Here are some of the core components that make up an effective fraud analytics strategy:

Data collection and analysis: Effective fraud analytics begins with comprehensive data collection, encompassing historical transaction records, user behavior, and real-time data streams. This data is then analyzed to identify anomalies that could suggest fraudulent activity.
Machine learning algorithms: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in fraud detection. Algorithms analyze vast datasets to recognize patterns that deviate from normal behavior, flagging potential risks for further investigation.
Real-time monitoring: Continuous monitoring of transactions allows banks to respond quickly to potential threats. Real-time fraud detection systems use predictive models to identify suspicious activities as they happen, enabling faster intervention.

How AI enhances fraud detection capabilities

Artificial intelligence is at the core of modern fraud analytics in banking, enabling institutions to process and analyze data at unprecedented speeds. AI's ability to learn from past fraud incidents and predict future occurrences makes it an invaluable asset in the fight against financial crime.

Supervised learning: In this approach, AI systems are trained on labeled datasets, learning to distinguish between legitimate and fraudulent transactions. This process enhances accuracy but requires significant data and human intervention for labeling.
Unsupervised learning: Unsupervised learning algorithms excel in detecting unknown patterns that could indicate fraud. They are particularly effective for identifying new types of fraud that have not been encountered before, reducing the reliance on predefined rules.

Common types of fraud detected by analytics in banking

Fraud analytics in banking is designed to address a variety of fraudulent schemes that pose significant risks to financial institutions. Below are some of the most prevalent forms of fraud that banks are combating with analytics:

Account takeover (ATO) attacks

These attacks involve unauthorized access to a user's bank account. Fraud analytics tools can detect unusual login attempts or changes in account behavior, alerting banks before significant damage is done.

Money laundering activities

Money laundering remains one of the most challenging issues for banks to detect due to its complexity. Fraud analytics can identify irregularities in large transactions or series of small deposits that don't fit the usual patterns, helping to prevent illicit activities.

Payment fraud in online banking

With the rise of digital payments, fraudulent transactions have become more common. AI-powered analytics tools can instantly flag and investigate suspicious transactions in online and mobile banking, allowing financial institutions to act quickly to prevent loss.

Best practices for implementing fraud analytics in banking

To maximize the effectiveness of fraud analytics, banks should follow these best practices for integrating these systems into their operations:

Invest in scalable data infrastructure

Building a scalable data infrastructure is essential for processing the massive amounts of information required for real-time fraud detection. Cloud-based solutions can enhance data storage, processing speed, and system flexibility.

Adopt a layered security approach

Combining multiple layers of security technologies (e.g., AI, machine learning, and behavioral analytics) increases the ability to detect and respond to sophisticated fraud attempts. A multi-faceted approach ensures that if one layer is compromised, others remain intact to protect the system.

Regularly update and train AI models

Fraud tactics evolve rapidly, making it crucial for banks to continuously update their AI models. Regular training with new data helps the algorithms adapt to emerging threats, ensuring that the fraud detection system remains effective over time.

Foster collaboration among departments

Fraud detection should not be limited to the IT or data teams. Involving departments like compliance, risk management, and customer service ensures a comprehensive approach to identifying and mitigating fraud.

Use data visualization for decision-making

Visual representations of data trends and fraud alerts can significantly aid in understanding complex fraud patterns. Dashboards and analytics tools that present data visually help teams make informed decisions faster.

"Fraud detection should not be limited to the IT or data teams—collaborating across departments ensures a comprehensive approach to mitigating fraud."

How to develop a banking fraud analytics program

1. Conduct a comprehensive data risk assessment

The first step is a thorough financial data analytics risk assessment. This isn’t a one-time exercise — it’s an ongoing process of continuously gathering and analyzing data to pinpoint areas that are more susceptible to fraud.

This step entails:

Data gathering and management

Banks first need to establish a system for collecting and storing vast amounts of data. This includes customer transactions, account details, device usage patterns, and historical fraud incidents.

This data needs to be meticulously organized and readily accessible for ongoing analysis.

Threat identification and prioritization

With the data consolidated and cleaned, banks can leverage AI’s predictive capabilities to identify potential security gaps and vulnerabilities.

For instance, a bank with a large online presence might prioritize identifying vulnerabilities related to ATO or online payment fraud. The identified vulnerabilities are then assessed for their potential impact on the bank, considering factors like the size of potential losses and the complexity of exploiting the weakness.

Rather than trying to boil the ocean, this prioritization allows banks to focus their resources on the areas that pose the greatest risk.

2. Develop an ongoing approach to fraud detection

Once vulnerabilities are identified, banks can shift their focus to detecting suspicious activities. Here are some of the techniques used at this stage:

Statistical analysis

Quantitative data models can identify anomalies in transaction patterns that may suggest fraudulent activities. For example, a sudden increase in high-value transactions in a short period could trigger a fraud alert.

Learning models

Machine learning (ML) algorithms learn from historical data to predict future behavior. This learning comes in two forms: supervised and unsupervised.

Supervised learning models

Supervised learning models are trained on labeled datasets to recognize fraudulent transactions. This method tends to be more reliable but also requires significant human labeling effort.

Unsupervised learning models

Unsupervised learning, on the other hand, excels at spotting anomalies — patterns that deviate significantly from established behavior. While it minimizes human intervention, it can occasionally misinterpret normal fluctuations as suspicious activity.

Risk scoring and investigation

Once activities are flagged by the model, they’re assigned a risk score based on the severity of the anomaly. High-risk activities are then prioritized and forwarded to fraud analysts for a deeper investigation.

This investigation may involve contacting customers for verification, blocking suspicious transactions, or even involving law enforcement agencies.

Deep domain expertise

In most cases, banks can’t conduct a comprehensive risk assessment alone. Enlisting the help of a partner with deep domain expertise can help banks ensure their programs incorporate the latest and greatest fraud detection techniques.

Achieving continuous fraud prevention improvements

To stay ahead in the fight against fraud, banks must adopt a culture of continuous improvement in their fraud prevention techniques. This involves:

Regular audits and updates: Regularly review and refine fraud detection methods to address any gaps in the system.
Staying informed on industry trends: Keep up with the latest developments in fraud tactics and prevention technologies to stay ahead of cybercriminals.
Collaborating with industry experts: Partnering with firms specializing in financial fraud analytics can enhance your capabilities and offer insights into the latest techniques for combating crime.

Best-in-class security with Lumenalta

AI-powered fraud analytics have given banks a potent tool for combating financial crime. But implementation can be complicated — the sheer volume of data, combined with constantly evolving fraud prevention techniques, means these systems need constant fine-tuning to work their best.

You need a partner to help you keep track of it all. That’s where Lumenalta comes in. Our team brings a wealth of software experience in financial services and can help you stay protected through a custom-built defense strategy.

You owe it to your stakeholders to keep your data and customer funds as secure as possible. Schedule a consultation today and see how Lumenalta can help you improve your security posture.

Table of contents

What is fraud analytics in banking?
The complex challenge of banking fraud
Key elements of fraud analytics in banking
How AI enhances fraud detection capabilities
Common types of fraud detected by analytics in banking
Best practices for implementing fraud analytics in banking
How to develop a banking fraud analytics program
Achieving continuous fraud prevention improvements
Best-in-class security with Lumenalta
Common questions about fraud analytics in banking

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