How AI-Powered Predictive Analytics in Finance Is Transforming Fraud Detection
Picture this. A major, global financial firm faces a sophisticated fraud attempt. The system notices an anomaly in a loyal customer’s account. The attempt puts millions at stake!
However, within just seconds of the attack shot, the AI predictive analytics finance system predicts a fraud instance and flags it as suspicious. It analyzes thousands of data points in real time, recognizes a deviation from the customer’s normal behavior, and freezes the transaction.
Thus, AI thwarts an attempt that could have caused a colossal, six-figure damage to the account holder and loss of priceless reputation to the institution!
This might seem like science fiction. It is surely science but not fiction. Precisely, it is predictive analytics in finance in action!
The Vulnerability of the BFSI Sector to Cyber Frauds and Attacks
Finance is one of the most vulnerable of all sectors to cyber fraud and attacks. It is because financial services have the highest number of exposed sensitive files amongst all industries. Bad actors constantly devise ways to conduct diverse attacks and cause spectacular damage to the target institution.
As for numbers, in the first half of 2024, India’s financial sector alone witnessed a 170-180% surge in phishing attacks compared to the same period last year. But the numbers alone aren’t just alarming. The value of damage is also. The global average cost of a data breach in the BFSI sector is nearly USD 4.90 million. That’s huge money, isn’t it?
Financial institutions have been combating cyberattacks for decades with traditional systems that are more reactive than proactive. Therefore, amidst the evolving cybersecurity environment, these institutions need more than just investigative platforms. They need a technology that helps them predict fraud, specifically the use of predictive analytics in finance, to stay ahead of threats.
Common Challenges Financial Institutions Face While Fighting Fraud
As economies grow, so do banks. However, this growth brings challenges that demand smart strategies and phenomenal technology leverage. Some common challenges that banks face include, but aren’t limited to, the following.
- Growing transaction volumes for which a manual review is practically unfeasible.
- Many banks don’t want to upgrade their rigid legacy systems that refuse to adapt.
- False positives disappoint legitimate customers, resulting in lost opportunities.
- Regulatory pressures demand continuous vigilance and transparency.
- Siloed data hampers the ability to conduct a holistic risk assessment.
Conventional rule-based systems were once new and effective. But they may not necessarily be relevant and useful anymore. New-age challenges and moreover, rapidly evolving cyberattacks demand more sophisticated techniques – precisely technologies that help prevent attacks rather than damage control or investigations.
The use of predictive analytics in finance is thus relevant and critical. It is imperative for every financial institution, whether big or small, regardless of the number of transactions it handles. Honestly, it is also because you wouldn’t know which bank, insurance firm, or non-banking financial institution is next on an attacker’s radar!
This is where AI-driven predictive analytics comes into play. Let’s see how it helps.
What is AI Predictive Analytics in Finance?
AI-powered predictive analytics uses ML and vast data ecosystems to anticipate fraud before it happens. Therefore, unlike conventional systems that merely detect anomalies, AI-based systems predict intent. They analyze historical data, real-time transactions, external indicators, and behavioral biometrics to identify dubious activity patterns.
However, what also makes these AI-driven models critical in today’s times is their ability to continuously learn, refine, and evolve. They learn from each incident and become smarter, enhancing an institution’s capabilities to deal with evolving cyberattacks better.
Finance predictive analytics proves to be useful and helpful in various areas and across many different areas. Let’s look at the most common ones.
How Does AI-Based Predictive Analytics in Finance Help – 8 Remarkable Ways!
From helping BFSI players score risk in real-time to anticipating threats, here are eight ways AI-based predictive analytics is the future of financial cybersecurity.
1. Real-Time Risk Scoring
Predictive models evaluate every transaction while it is happening.
For example, credit card fraud is one of the most common ones across the financial world. In 2023, these frauds caused losses worth USD 30-35 billion! Therefore, this is one area that requires financial organizations to be more cautious.
So, someone uses a credit card in somewhere in the world. But just a few minutes later, in some other part, AI instantly assigns a high-risk score.
The system cross-references the location data, device fingerprints, and spending patterns. If the transaction or activity is unusual or inconsistent with the customer’s typical behavior, the system triggers an alert or holds the transaction.
Such a split-second scoring action helps the bank act before the account is debited.
2. Behavioral Pattern Recognition
While numbers matter, fraud detection isn’t just about numbers. It is also about behavior.
Predictive analytics focuses on a user’s behavior by building a behavioral fingerprint. It does so by studying factors like the transaction timing, channels, and frequency.
For instance, a retiree usually makes insignificant or small purchases during the normal working hours or daytime. However, let’s say a transfer transaction worth USD 12,000 happens in their account at 3 AM. Now, the system, which continuously monitors each transaction, identifies this as suspicious behavior or a behavioral anomaly.
Therefore, instead of just tagging the transaction based on merely the amount, it comprehends the context and responds or reacts accordingly.
3. Adaptive Learning
Fraudsters, their intelligence, and therefore their attack types, evolve with time and technology. Therefore, your defense systems should also evolve and adapt.
The beauty of AI-driven systems is that they use ML to evolve in real time. ML helps the system to adapt and learn with every transaction and instance.
Therefore, if bad actors discover a new technique, let’s say targeting a digital wallet API, the system learns from a few incidents and auto-updates its detection logic.
Accordingly, the techniques that work for criminals today will not work tomorrow. The system would have evolved to tackle them intelligently and build guards against them.
4. Better Anomaly Detection
Conventional systems often use static rules like blocking transactions above USD 6000 or so. However, predictive analytics goes deeper and scrutinizes every transaction, not just based on the amount, but time zones, merchant IDs, IP addresses, and keystroke patterns.
Examining these factors helps the system discover and reveal intricate fraud rings. For example, fraudsters usually transfer small sums to a particular digital wallet to avoid triggering fraud detection systems or alerting the account holder immediately.
This is where AI steps in. It identifies the hidden link, preventing micro-fraud.
5. Reducing False Positives
False positives are amongst the most frustrating for legitimate account holders, the loyal customers of a particular financial institution. It is because false positives flag legitimate transactions as fraud, triggering a response that serves as a roadblock to the transaction.
Predictive analytics minimizes false positives by understanding each spending context.
For example, a frequent traveler makes a card payment in a new country. Now, instead of auto-blocking the new card, AI checks the person’s travel history, ticket bookings, and geolocation data. Now, the system knows the customer is abroad. Thus, it lets the payment pass, balancing security and convenience and maintaining customer trust.
6. Cross-Channel Fraud Monitoring
Cross-channel fraud is a popular technique where a fraudster attempts or executes a scam via multiple channels. Let’s look at an example.
A criminal hacks an online banking account, tests credentials through a mobile phone, and withdraws cash from an ATM. A conventional system may get confused or may not comprehend this multi-channel or cross-channel technique. But AI systems do.
AI-based predictive analytics in finance integrates data from different channels, correlating each data point and identifying patterns.
Another example is multiple futile attempts to log in and then a successful transaction via phone banking. The system triggers an immediate action and investigation in this case.
7. Compliance Support
Compliance in finance is complex and dynamic. Hence, dealing with it has never been straightforward. As a result, many institutions have traditionally struggled with it.
However, predictive analytics flips the script, particularly when it comes to fraud. While detecting fraud, it auto-generates audit trails and suspicious activity reports.
For example, if the system detects a money laundering pattern, it records the entire decision-making chain, including data inputs, model predictions, actions taken, etc.
Such transparency enables regulators to see how and why a system flagged a transaction, helping the institution comply with different laws.
8. Proactive Threat Intelligence
Predictability paves the way to prevention. Hence, instead of waiting for an incident to happen, the system anticipates it and provides insights to prevent it.
Predictive models analyze historical attack data, social media interactions, and even dark web activity to predict emerging fraud schemes.
For instance, phishing is among the most common attack forms in the digital world. So, when new phishing domains begin replicating a bank’s website, AI-based threat intelligence flags the risk before targeting the customer.
Connect with FIXSOL for Predictive Analytics in Finance
Cyber threats in the finance world are real and persistent. They are evolving at an unimaginable pace, requiring cybersecurity specialists to be super vigilant and a step ahead of cybercriminals. This is where the use of predictive analytics in finance becomes crucial. It examines each transaction comprehensively, helps predict fraud, raises real-time alerts, and enables more prudent action. By using predictive analytics, financial institutions can balance security, customer experience, and trust.
Would you like to know more about predictive analysis in finance ? Then, click to explore and email us at sales@fixsol.com to discuss your requirement in detail.