Hi, 

Welcome to the 116th edition of The Pattern, a weekly newsletter where we delve into the latest in the world of finance, technology and economy. Let’s get started. 

Financial scams and frauds in India are aplenty. And this week, RBI seems to have taken special note of one such scam that’s on the rise: Mule accounts. Governor Shaktikanta Das, in a meeting with top private and public sector banks, urged banks to step up their efforts against mule accounts. Banks were asked to amp up cybersecurity controls, manage third-party risks, and intensify customer awareness and education initiatives.  

This, however, isn’t the first time that mule accounts have been on the public radar. In November last year, six men were caught by Bengaluru Cybercrime officials for opening accounts across various banks to park money from cybercrimes. The police traced 160 mule accounts connected to 75 cases across the country.  

It’s safe to assume that mule accounts have been around for a while. The nexus clearly runs deep, with perpetrators using accounts of genuine citizens, some innocent and others hand-in-glove with fraudsters. Meaning that mule accounts slip through the cracks of the (otherwise) watertight KYC compliance.  

While digging deeper, I came across a BioCatch report that got to the bottom of the mules account phenomenon. It mentions that banks are likely aware of only 1 out of 10 mule accounts that exist. And when one takes a closer look, it becomes clear why: 

• Not all mule account holders have malicious intent – some are duped, and some are victims of account take overs 

• Those opening accounts with the intent of laundering easily pass KYC checks, as they are real or well-synthesised identities 

• Transactions passing through mule accounts don’t reflect unusual banking activity – not to the extent that they can be flagged by banks, anyway 

However, BioCatch noted that there are patterns to this type of fraud too – like location changes over time, the use of one device for several accounts, and typical persona-types who either indulge or fall victim to the scams. These are activities that can be detected via consent-based extraction of device data. As for tracing the larger patterns of fund transfers, can a transaction analysis-like model be used? 

So here is my more-than-obvious solution to the mule account problem: AI-based transaction and alternate data analysis! It’s something most players in the ecosystem already have in place, albeit in a different avatar. Can they be used to map, detect, and prevent mule account activity when they happen? The intricacies need to be thought out, of course – but I think it isn’t too far-fetched!  

P.S. If you’re a lender who’s looking for someone with deep expertise in building both transactions-based and alternate data-based risk assessment tools, FinBox can help. Explore transaction and alternative data -based risk assessment tools here. Or better still, get in touch with us !  

That’s all from me this week. As always, leaving some interesting reads below.  

Reading list   

• ONDC looks to integrate banks and fintechs for last mile credit services    

• How Sebi action will impact profits of discount stock broking firms like Zerodha and Groww    

• Indian banks are rising on global stage, but investors are yet to join the party  

   

  
  

• Indian banks see climate change as their biggest source of systemic risk  

Thank you for reading. If you liked this edition, forward it to your friends, peers, and colleagues. You can also connect with me on Twitter here and follow FinBox on LinkedIn to always get all updates. 

Cheers, 

Mayank