Spotlight: Fraud Analytics

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Is there an unauthorized payment transaction to a vendor?

Does a suspicious amendment to banking details show up in payroll or supplier data records?

Can weakness within the client system be identified to prevent fraud recurrence?

How to detect SIM card cloning?

With increase in the use of net, mobile, IVRS (Interactive Voice Response System), ATM, commercial and retail electronic transaction across various sectors (like banking, insurance, retail), manual systems and personal supervisions are no longer sufficient to detect or prevent fraud. This has driven a need for data mining and analysis techniques that uncover patterns or anomalies in transactions, customer applications and records. Besides, business reorganization or downsizing can weaken the information system of an organisation and present opportunities of fraudulent financial reporting and asset misappropriation.  During economic downturns, the incidences of fraud increase, calling for real-time monitoring and controls of fraud. Use of Fraud Analytics in such scenarios facilitates transaction monitoring, risk profiling and client verification for minimal losses to the organisation..

Defining Fraud Analytics

An integrated methodology that uses statistical methods, techniques of analysis and data mining, with sophisticated tools or dedicated software; for continuous monitoring, investigation, detection and prevention of fraudulent transactions.

Industry verticals using Fraud Analytics: Banking, Financial Services, Insurance, Retail, Telecommunications

Data mining models, statistical analysis techniques using correlation, trend and time-series, behaviour profiling, peer analysis, artificial intelligence (AI), predictive modelling, link, collaborative and interactive analyses, transaction monitoring and risk profiling approaches, are various tools of Fraud Analytics applied for an integrated Fraud Management.

Key roles

Real time alerts of fraud

Detecting fraudulent patterns

Eliminating new frauds

Minimising losses from fraud

Protection against habitual offenders

Internal fraud monitoring

Supports forensic accounting

The bottom line is that, the ability to quickly detect and deter fraudulent activity without impacting customer base or internal workforce culture, has a direct bearing on the growth and profits of an organisation. A great case study that exemplifies why Fraud Analytics is core to an enterprise is that of Enron.

With systems becoming more complex, organizations are deploying customised enterprise-wide real-time fraud solutions that offer flexible architectures and operational environments for fraud detection and prevention. As the market for Fraud Prevention and Detection increases, the career prospects in this niche analytics field are tremendous for those with a fascination for post-operational analysis and trouble shooting, or software developing skills.

Suggested Reads:

Fraud Analytics: Strategies and Methods for Detection and Prevention (John Wiley & Sons, Published 29 November 2013)Delena D. Spann

Forensic Data Analytics: The importance of Data Analytics in Fraud Detection and Prevention – Deloitte

The Role in Data Analytics in Fraud Prevention – Ernst & Young

Using Analytics for Insurance Fraud Detection – Infosys

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