NAVIGATE BANK FRAUDS THROUGH PRE-EMPTIVE DATA ANALYTICS

Reputation for integrity has proven to be a vital factor in safeguarding market confidence and public trust. While such efforts maximise the efficiency of security circuits, unforeseen instances can undermine the whole scenario. This factor is significant, especially in an environment marked by intense scrutiny and rising enforcement.

Financial institutions face the constant threat of frauds. A financial fraud attack can breach the internal & external processes alike. With threats becoming increasingly sophisticated, it’s time to rethink your fraud detection & protection strategy frameworks. Organisations with high-performance index accept risks & execute pre-emptive measures while flipping frauds into opportunities.

Before diving deeper into the ways in which frauds can be mitigated, let’s take a closer look at its effects on customers and means to detect them before-hand.

Frauds are bifurcated into misappropriation of assets by employees and fraudulent financial reporting by management. Both the types have a devastating & holistic effect on the organisation.

How frauds affect customers?

Frauds expose organizations to legal, regulatory, or reputational damage. Improve organizational decision making with analytic solutions, services and resources that turn fraud and risk analytics into opportunities. Align your financial fraud management strategy to suitable predictive analytics models for optimum results. Here are more ways in which frauds affect the customers;

Financial Loss – The costs of fraudulent financial reporting are harder to determine. Fines assessed for misleading investors, civil suits to recoup investors, and creditor losses add up to severe organisational losses.

External Confidence –  As soon as the fraud is uncovered, the company faces an ongoing issue of public trust. This leads to lack of belief from the stakeholders and might be barricaded from strategic alliance, considering the reputation.

Company Morale – The effects of a fraud on the company’s overall environment can be shattering. Internal employees may cross roads with embarrassments. Not just that, if an employee leaves the organisation, he might carry the image externally.

Increased Audit Costs –Financial institutions that come under the radar of fraudulent activities house experienced risk auditors. If the fraud was perpetrated by the top management, audit costs can soar to unseen heights.

High performing organizations invest hugely into end-to-end solutions, which detect threats prior and eliminate potential risks. They proactively build strategic and pre-emptive capabilities, which are precisely focused on real business benefits.

How are frauds detected?

One way to approach the fraudulent detection is to consider it a predictive modelling problem of anticipating a rare event. The primary goal is to identify the best practices and formulate a validated model, which maximises the likelihood of the observations to be associated with frauds. Consider this, if historical data directs at fraud identification & verification, the typical predictive modelling workflow can be pointed at increasing the chances to capture those opportunities.

Here are ways in which frauds can be kept under check;

Prediction of Attack Patterns

Fraudsters, even the most sophisticated ones, are known to have a revealing attack pattern, similar to events or happenings. For instance, tax scams are more prominent during tax season. Using predictive analytics banks can anticipate the threats approaching and enable the customers to be hassle free.

Data Triggers Quicker Resolutions

Fraud detection is the most essential element of minimizing losses. When banks have the capabilities of faster threat detection, it can restrict account activity. Fast fraud detection is essential to maintain long and healthier customer relationships.

Data Analytics: The way to Fight Financial Frauds

Financial institutions constantly face fraudulent transactions. With effective data analytics & quicker detections, banks can now identify fraud cases faster. Consider the case, ‘Punjab National Bank’s use of AI & Data Analytics to detect frauds & plug financial. The bank announced its plan to implement Artificial Intelligence (AI) in account reconciliation powered with data analytics to improve its audit system.
Taking pre-emptive measures powered with enhanced data analytics, enables the eco-system to predict threats and nullify the effects it has on customers.

By Kastle Banking AMLOCK | Blog, Analytics & Fraud Detection