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Inline Fraud & Risk Management (IFRM)

IFRM Overview

BANKiQ IFRM is a real-time, Cognitive AI-ML enabled Fraud Prevention solution addressing various aspects of Financial Crimes. Banks, Financial Institutions & Payment solutions providers require Risk Management solutions which are real-time and Preventive in nature, as also the ability to blend & embed into the various Payment processes. BANKiQ IFRM is a Fraud Prevention solution for an Issuer, Acquirer, Banker, Lender, FinTech – Payment Gateway Providers, Payment Aggregators & Merchant Acquisition Fintech’s.

BANKiQ IFRM supports detection of multi-channel fraud. It uses a multitude of fraud detection strategies including Adaptive Analytics, Deep Learning, AI, Customer and Transactional Profiling, Behavioural Rules and other probabilistic models on a cross-channel data agnostic basis, both in real- time and batch.

The solution provides the following functionalities including but not limited to strong real-time Link Analysis and Data Visualization, comprehensive Cross-Channel Alert and Case Management, Work Flow, Fraud and Operational Reporting, Granular Security, Auditing and Extensive Performance management along with interjection, powerful orchestration and operational control, reporting and visualization through a user-friendly graphical console. BANKiQ – IFRM platform has a rich variety of features including AI and deep learning, bespoke analytics, a rich rule engine and industry leading forensics around the data, link analytics and geo locations, all in an easy to use analyst friendly form factor.

01

Problem Statement

Banks are expected to offer a seamless customer experience while also stopping Fraud in its tracks. Banks are required to balance these goals and manage risk effectively in the Digital Banking & Fast Payments era. Traditional rule-based solutions are too sluggish, reactive and dependent on humans, to be effective.

Whether it is connecting the dots to identify emerging Fraud patterns or supporting instant transfers 24x7, without raising the risk or offering new age digital payments without risk of money mulling; Banks are expected to seamlessly provide services, be compliant and prevent financial frauds with equal efficiency.

02

Core Proposition

We’ve built a powerful solution for Enterprise Fraud and Risk management with foresight into next-generation Fraud prevention technology.

We look into transactions and events with machine learning and evaluate risk for decisive action. Banks can combine conventional rules management and cognitive intelligence for an effective step-up response which prevents frauds without compromising customer experience.

BankIQ IFRM is real time and precise, enabling Banks to offer a seamless Customer experience while also stopping fraud in its tracks and managing risk effectively in the Digital Payments & Fast-Payments era.

03

Functional

Banks are expected to effectively manage frauds, reputation and regulatory pressures. Banking fraud is not a matter of IF, but a matter of WHEN, it would be INTERNAL as well as EXTERNAL.

BankIQ IFRM combines behaviour profiling and transaction risk scoring to offer a comprehensive detection and response system that can help banks prevent frauds.

BankIQ IFRM can be applied to all systems which engages customers and employees, and all transactions across the systems, payment modes and channels, to effectively detect external and internal fraud threats. You stay on top of banking frauds protecting your customers, while keeping false positives to a minimum ensuring the frictionless customer experience.
04

Technical

BankIQ IFRM leverages fast data platform together with a curated set of technologies including data streaming engines, a data backplane, reactive microservices, persistence, machine learning and purpose-built tools that make an effective application.

Machine learning in BankIQ IFRM leverages tens to hundreds of data attributes, enabling financial institutions to identify pervasive and subtle threats, even when there are changes in context.

In addition to augmented fraud detection, machine learning-based systems also learn legitimate behaviours that improve customer experience by reducing the disruption of daily legitimate activities.

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