The client, an online remittance service provider, faced issues with fraud and money laundering that slowed transaction processing. They developed a risk scoring model using transaction history and other data to automatically classify transactions as low, moderate, high, or extremely high risk. Transactions classified as moderate or higher risk would require manual review, reducing review volume by over 500 per day and cutting processing time in half. The model helped compliance staff focus their manual reviews on higher-risk cases.

1. Business Situation:
Being a service provider of online remittances, the client faced significant problems with fraud and money laundering. A lot of
time was being invested in manual review of all transactions, which in turn delayed the overall transaction processing time,
and also impacted customer satisfaction.
The Task:
To develop a process where only high risk transactions are sent for manual review, and rest are approved automatically.
The Analysis:
• Transaction history, compliance, service data along with external data sources like SSN responses and AMLOCK (Anti-
Money Laundering Database) data for high risk Customers was leveraged
• Critical variables defined that would serve as inputs to the Risk Score were divided into 5 broad categories - Geographic,
Historic, Identity, Transaction and Demographic - to make it easier to understand the reason for high risk during manual
review process
• Along with these variables some rules were also created based on compliance policy and Government regulations, which
when triggered automatically moved transaction to manual review irrespective of the risk score
• Multiple linear regression was performed to arrive at a transaction-level Risk score, and 4 Risk classes were
correspondingly defined (<25: Low, 25-50: Moderate, 50-75: High, 75-100: Extremely High)
The Implementation:
• A schedule of data extraction was setup to generate the Risk score before the KYC forms were prepared.
• The process was streamlined to ensure that all KYC forms carried a Risk score
• The Risk score and Risk class were populated on the KYC sheets
• Compliance committee continues to give it’s opinion on each KYC form (Low Risk to Extremely High Risk) and the
predictive model continues to be bootstrapped
The Result:
• The new Process reduced number of transactions reviewed manually from 700-800/day to 200/day, and also reduced the
average turnaround time for transactions from from 3.0 to 1.5 days.
• The process also helped the compliance team to identify which documents/clarifications to get from customer to process
the transaction.
Analytics in Action
Transaction-level Risk Assessment
Client : A Leading Service Provider of Online Remittances

2. YOUR PARTNER FOR
DATA ANALYTICS SERVICES
MANAGEMENT TEAM
GLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. Cherian
CEO
Roy has over 20 years of rich experience in marketing, advertising and media
in organizations like Nestle India, United Breweries, FCB and Feedback
Ventures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhD
COO & Head of Analytics
Anunay has over 15 years of experience, with a significant portion focused
on Analytics in Consumer Finance. In his last assignment at Citigroup, he was
responsible for all Decision Management functions for the US Cards
portfolio of Citigroup, covering approx $150B in assets. Anunay holds an
MBA in Finance from NYU Stern School of Business.
Buck Chintamani
EVP, Strategic Initiatives & Business Development
Buck has extensive experience working with global clients across sectors.
He was an early employee at Infosys, a founding team member at supply-
chain software startup - Yantra, and part of the management team at RFID
sector startup - Reva. Most recently, he was the Vice-President for Service
Partner Strategy and Programs at product lifecycle management software
company, PTC. Buck has an MBA from IIM Ahmedabad.
Kakul Paul
Business Head, CPG
Kakul has over 6 years of experience within the CPG industry. She was
previously part of the Analytics practice as WNS, leading analytic initiatives
for top Fortune 50 clients globally. She has extensive experience in what
drives Consumer purchase behavior, market mix modeling, pricing &
promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
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