Demonetisation is after all a battle against money laundering, even if everyone is divided in their opinion. That said, it is the first time that the Indian government is going after the parallel economy by banning denominations of Rs 500 and Rs 1,000 to weaken contraband money.
Meanwhile, a bunch of Indians have built an artificial intelligence-based anti-money laundering platform to fight the global money laundering industry, which is worth $2 trillion. The platform uses AI to track money going into fictitious accounts through digital transactions from across the global banking network.
Srikumar, Suresh Nair and Mallinath Sengupta were happy in their corporate jobs till they realised that the capital markets were spending loads of money on compliance and yet could not figure out how to battle money laundering.
An IDC report says financial services IT spending will reach almost $480 billion worldwide in 2016, with a five-year compound annual growth rate (CAGR) of 4.2 percent. This money is used by the financial services industry to trace every possible route of money. Yet, 95 percent of the time money launderers outbeat the system by opening multiple fictitious accounts and companies across the world. Therefore, the trio created NextAngles, which is building an expert system to tackle this persistent issue.
“The core intelligence comes from the knowledge models which are deep domain models on particular aspects of banking work. This is different from conventional big data-driven data science. The core aspect of such expert systems is a branch of science called ‘ontology’,” says Mallinath.
Ontology in computer science looks at the relationship of that which is (money), the properties that interact with it (banks) and the events that change the relationship (money laundering activity).
The laundry cycle or the money laundering cycle
The cycle involves placement, layering and integration.
- Cash from illegal weapons, prostitution and drug sales are collected and deposited into multiple accounts
- Cash is wire-transferred to several companies titled X
- X transfers money to offshore banks
- Banks give loans to ‘Y’ fictitious companies
- Y pays false invoice generated by ‘X’.
The founding team
The 30-member data science team of NextAngles is, over the last two years, working on this predictive platform. They are conducting pilots in a couple of companies in the US and have plans to scale up the product starting next year. The product works in identifying fraud in banking activity (money laundering) by discovering hidden relationships and establishing patterns of money movements. For example, in case of an Internet fraud complaint, the solution looks for cases where money gets withdrawn from newly-opened accounts very quickly after a wire transfer, and then tries to find the relationship between these account holders to check if fraudulent activity is going on. While the system does this in hours, doing it manually would take days or weeks, depending on the complexity.
“Building the product and getting the financial backing was not easy,” says Mallinath.
The founding team created 19 ideas around the banking, financial services, insurance (BFSI) sector and discussed every cloud-based service for the industry. Being businessmen and technologists themselves they decided to take on the task of building an anti-money laundering platform for the banking industry, which does not have a powerful tool to battle perpetrators.
The three co-founders have 75 years of corporate experience between them. Mallinath is an IIM-B alumni, Srikumar is an alumni of XLRI and Suresh finished MSc in Computer Science at Trinity. They all worked in Mphasis when they decided to go up to the CEO, Ganesh Ayyar, and discuss this idea of taking on money laundering. Ganesh told them that if they had to do this startup then they had to answer the fundamental question, ‘Why would the customer buy it’ and ‘How would the product drive value?’
“The tough questions asked by Ganesh moulded our idea. We spoke to over 400 banks and realised that they had no tools to track patterns and banks always threw people to seek fraudulent patterns and went after the wrong people. That’s when we realised that we had an opportunity. They would buy the product because we would identify fraud faster than manual systems,” says Mallinath.
“Such a system would deliver value for business and reduces effort for the financial industry. Next Angles is now part of the Cart Up programme of Mphasis and has all the support to scale up,” says Ganesh.
Mallinath and their team of data scientists have raised $11 million (from Mphasis) so far and have built three solutions for the BFSI industry.
According to the United Nations Office on Drugs and Crime the total money laundered annually is around $2 trillion, which NextAngles is going after. The NextAngles solution will be implemented in banks and the system studies all the payment and withdrawal mechanisms in the bank to identify suspicious activity.
According to a study done by the firm, US-based regulator Financial Industry Regulatory Authority (FINRA) issued its annual Regulatory and Examination Priorities letter to highlight the key issues of management of conflicts of interest, technology, outsourcing and anti-money laundering (AML), which could adversely affect market integrity and therefore need focus. In May 2016, FINRA announced it had fined an investment management firm and its holding parent company a total of $17 million for what FINRA described as, “widespread failures related to the firms’ anti-money laundering (AML) programs.” While Indian consumers are led to believe that fintech payment companies are an evolution, wallets are not going to stop fictitious money transfers because money can be transferred through a multitude of accounts. This is where Next Angles has created a gold mine for itself, because it can lead the four functions of AML software, which are:
- Transaction monitoring
- Currency transaction reporting
- Customer identity management
Next Angles faces competition from the likes of Oracle, Digital Reasoning and Wynyard. But with the backing of the $1-billion Mphasis Ltd, it has the chance to become a larger entity. Now it is time for banks to use algorithms to fight money laundering rather than laying complete focus on just figuring out their most valuable corporate customers.
- Artificial Intelligence
- Big Data
- Mallinath Sengupta
- R Srikumar
- Suresh Nair
- Next Angles
- Money Laundering
- Anti-money laundering
- FINRA acts on money laundering