Fraud Analytics

Overview

Consulting in Fraud Analytics

Fraud is an escalating threat for banks. Technological advancements and changing customer preferences have opened up new avenues of banking for modern consumers but these channels of convenience have also attracted massive threats from fraudsters. With criminals and fraudsters continuously coming up with new ways to dupe financial institutions, financial firms now face ever increasing challenges of detecting criminal activities and fraudsters to protect themselves and their customers.

Financial Institutions are exposed to Fraud at different stages of customer life cycle. We provide analytical solutions to our clients to predict these frauds.

  • Origination - Identity Fraud, Application Fraud, Sleeper or Bust Out Fraud
  • Customer Management – Money Laundering, Transaction Fraud & Account Takeover Fraud
  • Collections – Non-collectable Debt Fraud, Repayment Avoidance Fraud

Our experts help the clients in detecting fraud and criminal activities by providing analytics such as:

  • Customer Profiling
  • Predictive Analysis
  • Behavioural Analysis
  • Network Analysis
  • Customer Profiling
  • Predictive Analysis
  • Behavioural Analysis
  • Network Analysis

We use Big Data analytics extensively to help our clients for real time scenario generation and real-time analysis for identifying potentially fraudulent transactions or criminal activities. Big data based services are contingent on the system capabilities of the clients. We support our clients to embed big data analytics IT capabilities for managing fraud.

Offerings

Our Offerings towards Fraud Analytics

Identifying Fraud

Fraud can be described as use of false representations to gain unjust advantage, dishonest artifice or trick.Few of potential fraud customers can be described as:

  • Mr A: 18-year old applicant quoting a mobile phone on contract that was taken out 5 years ago in his name
  • Mrs B: a 42-year old self-employed carpenter with an employment address 400 miles away from his home address
  • Ms C: 26-year old, employed as a teacher at ₹ 2,50,000 per annum, applying for a credit line despite having over ₹10,00,000 of unused available credit instruments

Identifying Fraud

Fraud can be described as use of false representations to gain unjust advantage, dishonest artifice or trick.Few of potential fraud customers can be described as:

  • Mr A: 18-year old applicant quoting a mobile phone on contract that was taken out 5 years ago in his name
  • Mrs B: a 42-year old self-employed carpenter with an employment address 400 miles away from his home address
  • Ms C: 26-year old, employed as a teacher at ₹ 2,50,000 per annum, applying for a credit line despite having over ₹10,00,000 of unused available credit instruments

Classification

  • First Party Fraud - Fraudster misinterprets his/her own details or fabricates a fictitious identity
  • Third Party Fraud - Fraudster misinterprets an individual details and purports to be the true owner of the identity

Fraud Management Solution

We provide enhanced security solutions that help in reduced chances of phishing attacks on customers by providing additional layers of authentication and minimizing time to detection. Real-time fraud prevention and detection uses automated analytics to identify suspicious behavior based on metrics that include location, transaction frequency and transaction size. The solution identifies and acts on new unusual customer behavior; rapidly creating and applying new fraud rules in real time.

We provide enhanced security solutions that help in reduced chances of phishing attacks on customers by providing additional layers of authentication and minimizing time to detection.

Real-time fraud prevention and detection uses automated analytics to identify suspicious behavior based on metrics that include location, transaction frequency and transaction size. The solution identifies and acts on new unusual customer behavior; rapidly creating and applying new fraud rules in real time.

Application

We use Customised Fraud Scorecards along with the applicant’s credit history to provide a 360° view of credit worthiness of the applicants.

  • Fraud Score and Application Risk Score can be use in conjunction through the use of matrix that combines both scores/risk grades
  • Greater accuracy in application strategies where risk is considered
  • The combined scores relate the observed good/bad and frauds/non-fraud odds to greater refine accuracy
  • Fraud Score and Application Risk Score can be use in conjunction through the use of matrix that combines both scores/risk grades
  • Greater accuracy in application strategies where risk is considered
  • The combined scores relate the observed good/bad and frauds/non-fraud odds to greater refine accuracy
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