Parikshit has over 17 years of experience in Fintech and leading engineering / product teams in cloud native, mobile applications, SaaS, DevOps and Cloud Computing in Canada and USA. At StashFin, Parikshit leads the Technology build out & oversees the data science initiatives. Parikshit is an AWS certified solutions architect & an avid technology enthusiast. Parikshit started his career in Canada in the Banking sector where he was building derivatives trading & risk management platforms, he then moved on to start an enterprise mobility venture called 6DegreesIT deploying a custom big data analytics platform onto enterprise mobile applications for three $5bn+ companies.
With the exponential rise of fintech and monetary growth volumes due to digitization, instances of digital fraud are at an all-time high. The spike in digital transactions has made fraudsters smarter and more tech-savvy. Traditional methods can no longer rein in those who can execute high-speed hacks and other scams.
With an increase in digital payments and the dependency on the digital economy during the pandemic, the rate of digital fraud attempts originating from India against businesses has risen by 28.3% and the highest percentage of suspected fraudulent transactions originated from Mumbai, Delhi and Chennai, according to a report by credit reporting agency TransUnion.
Digital fraudsters are not just attacking individuals, they are targeting businesses by using sophisticated technology and hacking tools. To curb this negative disruption, the first step to master is fraud detection. The financial industry – from retail to insurance and digital lending – simply can’t ignore it any longer.
Deploying AI and ML
As we develop new technology and advance with superior interfaces and processes, we are also facing several challenges associated with it. Hackers are targeting loopholes and duping unaware consumers to commit nearly impossible-to-detect frauds. It’s not just their scale, but also their complexity – financial institutions and organizations apply several strategies to detect them, but they become obsolete within days. In this situation, there is a lot to learn from emerging fintech companies.
Fintech startups are deploying artificial intelligence (AI) and machine learning (ML) algorithms to create self-learning systems that keep up with changing realities and increasing complexities. Since it is nearly impossible to detect or predict fraud patterns, AI- and ML-driven systems allow us to identify transactions that are suspicious. This allows organizations to detect frauds even for immensely large volumes of transactions, which is impossible to do manually.
ML also eliminates another challenge – false positives, which can affect genuine transactions and lead to poor customer experience.
How do they work?
When someone steals your identity and financial details to avail a loan, it might be difficult to prove to the authorities that it wasn’t you. You may not even be aware of the fraud until one day you apply for a loan and it gets rejected due to a poor credit rating.
By deploying AI and ML algorithms, such identity theft can be prevented. ML models use pattern identification to significantly improve the accuracy of fraud detection. Even if credit is issued, lenders can track spending patterns to determine a possible case of identity theft. Similarly, AI models combined with image processing can identify forged documents that can block fake borrowers.
What lies ahead
There is no doubt that data is one of the most valued commodities of the 21st century. With rapid digitization and internet penetration, data – public or private – has a wider footprint than ever before.
While data can deliver immense value, it also bears several risks. This can be a problem because it is the most important input for AI and ML systems. Thus, due to the sensitivity around data and the need to protect it, organizations must ensure fool-proof protection by combining physical, electronic, and procedural inspections.
The use of AI and ML for fraud detection is entrenched already. Fintech companies are enabling ML models to analyze large volumes of data to make decisions and identify fraud. Companies are also using annotation tools and services to dive deep into data analysis and build models to identify fraud as early and precisely as possible. Fraud detection, with or without AI and ML, requires consistent study. AI and ML, supported by tools like computer vision, are cost-effective, faster, accurate and real-time, creating a layer of protection for service providers and customers.
AI and ML, thus, enable service providers to deliver the best financial solutions and enhance the customer experience manifold.