The Power of Fraud Analytics in Banking

The banking sector of today relies on digital solutions to manage customers and provide an overall better credit and lending experience. While technology makes business processes simpler and more advanced, it also makes a company vulnerable to fraudulent customers and non-performing loans (NPLs).
Non-performing loans are one of the major factors that interfere in bank stability and growth as well as reducing economic efficiency. For banks and financial institutions, the immediate consequences of an increase in NPLs are higher requirements in capital to absorb potential losses and the resulting rise in management, funding, and administrative costs.
Hence, fraud analytics in banking has become essential in detecting illegal practices at the early stage and taking precautionary measures to prevent substantial irreversible losses.

FinScore Uses Machine Learning to Prevent Fraudulent Borrowers

What is Fraud Analytics in Banking?

In a nutshell, fraud analytics combines analytic technology and big data for banking with human interaction to help detect potentially or untrustworthy loan applicants who declare false information just to get approved.

Using data analytics to detect fraud involves gathering and storing relevant data and mining it for patterns, anomalies, and discrepancies. The results are then translated into insights that allow banks and financial companies to prevent potential threats and NPLs before they occur. It also enables them to develop a proactive data-driven fraud detection environment

Analytical Techniques for Fraud Detection

To get started, you need an understanding of:

  •  The areas where fraud can happen in the lending business
  • What fraudulent activities or practices would appear like in the data
  • The data sources you need to test for indicators of fraud or the possibility of NPLs (e.g., geolocation, contact persons of potential fraudsters, and existing fraudulent customers who got blacklisted in the past for suspicious or illegal activities)

Why Use Data Analytics for Fraud Detection in the Banking Sector?

The world has changed. The bank fraud controls you have may no longer be relevant. Nowadays, almost everyone engaged in fraud leaves a trail of digital footprints behind. This presents companies with a big opportunity to prevent further harm by using data analytics to detect fraud.

Leaders in alternative data driven fraud detection are also taking advantage of new technologies and tools to identify instances of fraud, before they occur and cause drawbacks to banks.

This development could not occur at a better time — as there is a steady increase in the number of individuals attempting to take advantage of the quick and easy approvals and disbursements of banks. It is a different world out there and fraud analytics in banking can help you make sense of it all.

More data, more opportunities

Rules-based methods and anomaly detection have been in widespread use to combat fraud While they are powerful tools, they still have their limits. Adding data analytics to the mix significantly expands fraud detection and prevention capabilities.

Not only can data analytic tools enhance rules-based testing methods, but they can also help measure performance to help fine-tune and standardize controls for constant improvement. That is a big deal for banks and companies submerged in data – data that can be put to better use.

The Benefits of Fraud Detection in Banking

Below are some key benefits that data analytics offers regarding bank fraud prevention and detection.

Identify hidden patterns

Traditionally, businesses often fail in identifying unusual or irregular patterns of practices that may result in fraud. With the help of fraud analytics in banking, companies can identify new patterns, scenarios, schemes, and trends that influence fraudulent activities and traditional approaches miss.

Enhance and extend existing efforts

Rather than completely throwing away the traditional rules-based methods, the new fraud detection tools in banking can add up to the existing efforts of your enterprise to bring you better results. They add an extra layer to enhance your fraud detection and prevention efforts.

Cross the divide

Fraud analytics plays a vital role in the integration of data. It combines data from public records and other various sources into one central platform. By integrating the data into a intelligent model, you help create a true, enterprise-wide approach.

For instance, FinScore’s Social Circle Fraud Tool instantly discovers links between loan applicants and previously identified fraudsters. Alongside the information gathered from public records, you can verify if an applicant has contacted a known fraudster, which can be a strong reason to reject their application.

Deriving Value from Unstructured Data

Today, many data analytics companies who provide fraud analytics in banking partner with enterprises who have thousands or millions of datasets that paint a cohesive and overall better picture of a borrower’s profile. Thanks to the power of AI/ML technologies, you too can leverage the power of greater indicators and data for fraud prevention.

Measure and improve performance

Fraud analytics helps banks in rightly identifying what strategies will suit their operations best and which ones will not. In this manner, it allows banks to choose the right strategy that will enhance their performance. With fraud analytics in place, you do not have to guess. Your data tells your story.

Power Up Your Fraud Detection Techniques with FinScore

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Technology has definitely made digital credit scoring and transactions simpler and more convenient, but it has also enabled fraudsters to commit synthetic fraud when applying for loans, savings accounts, and other banking solutions. Relying on traditional sources of data can lead to fraudulent activities getting overlooked.

Many banks and financial institutions in the companies have turned to FinScore’s fraud verification tools to distinguish fraudulent customers from legitimate ones. We do this by leveraging the power of alternative telco data to help you make better-informed decisions. Our range of fraud detection tools include:

  • Geo Location Fraud Tool – matches an applicant’s declared principal residence verses the various geo-locations provided by their telco provider. It helps you assess an applicant’s honesty during loan applications when they provide contact details.
  • Contact Person Fraud Tool – leverages on telco data to compare an applicant’s most often called or texted mobile number to the Contact Person on their application. It can be used to determine their trustworthiness.
  • Social Circle Fraud Tool – compares a person’s most often contacted mobile numbers with an existing Fraudlist/Blacklist provided by a partner. If any names match with an applicant’s most often contacted numbers, this can be a strong reason for rejection.

Our clients include consumer banks, thrift banks, digital banks, digital lending platforms, and consumer lenders. You too can prevent fraud from causing damage to your bank. Contact us today to learn more about our fraud detection tools.

FinScore is a financial technology company in the Philippines that offers a powerful credit scoring platform and fraud detection tools based on alternative data, including telco-based data. 

As the pioneer in lending and scoring of the unbanked, we continuously provide fintech services that empower financial institutions, banks, and credit bureaus with flexible platforms to help them make insightful and reliable credit decisions. Contact us today to learn more about our products and solutions for financial institutions.