– By Ritesh Srivastava
The digital payments ecosystem in India has grown significantly in recent years driven mainly by e-commerce, mobile, and contactless payments systems. With expanding merchant acceptance and digitization of value chains, the growth momentum of digital payments is likely to continue unabated in India. In fact, the digital payments market in India is expected to touch INR 280.84 trillion by 2027, as per a report by ResearchAndMarkets.com. The digital payments market is projected to continue growing at a CAGR of 15.73% till 2027, as per the report.
However, this growth in digital payments has come with its own set of challenges. The exponential increase in digital payments has led to an increase in instances of online fraud. According to data from the payment intelligence company, Fraugster, e-commerce losses to online payment fraud are expected to reach USD 48 billion in 2023 globally and grow by 131% from 2022 to 2027. In India, too, online payment frauds are on the rise. In its annual report for FY23, RBI had mentioned that card and internet frauds were worth $1.55 billion, as compared to $1.19 billion a year ago.
With the rise of digital payments, frauds are increasing by the day, and across industries. For a fintech company, instances of fraud can not only affect its prospective revenues but also cause harm to its brand reputation and customer loyalty. Apart from the direct financial impact of fraud, there is an indirect cost that a fintech bears due to the loss of opportunity and reduced trust amongst the customers at large.
To better meet the exacting demands and preferences of their target audience, fintech platforms collect data via thousands of data points at different interventions ranging from personal information, and bank statements, to occupational details and analyze them for refining their services and products. As FinTech rides upon a lot of Personal Identifiable Information (PII) it makes them extremely vulnerable to data theft. The sensitive nature of the data and real-time constraints necessitate robust data protection and security mechanisms for ensuring data protection and prevention of fraud in a fintech.
Role of Fraud Analytics in preventing online fraud
The use of data analytics in the identification and prevention of financial fraud is what is called Fraud Analytics. As online payment fraud is evolving and becoming increasingly sophisticated, the traditional transaction-centric, anti-fraud method of detecting fraud is no longer effective. With the increasing pace and volume of fraud threats, financial Institutions are integrating advanced analytics into their fraud defense mechanism and are dedicating additional resources to build advanced analytics models for preventing and mitigating fraud. Fraud analytics uses big data techniques to detect and mitigate real-time fraudulent activities and can also predict future behavior and suggest preventive methods.
Different Fraud Analytic Techniques
The last few years have witnessed the advent and increased use of various forms of fraud analytics techniques by fintech companies, with the objective of reducing fraud and in turn, enhancing trust in the new-age fintech products. Here are some of the preferred fraud analytics techniques used by fintech and financial services companies.
Predictive Analytics: Predictive analytics is the use of historical and current data to make predictions about the future through statistical modeling, data mining, and machine learning algorithms. It helps uncover hidden fraud risks by identifying patterns in large datasets and taking proactive action to prevent them. This helps reduce predictable frauds as fintech can ensure that they take corrective action well in advance. Also, fintech can build smarter fraud score algorithms and model more accurate fraud prevention strategies by analyzing past data, trends, and variables.
Pattern recognition: In pattern recognition, fraud is identified through the identification of activities that are astray from the norm using a Machine Learning (ML) algorithm. Since ML algorithms are designed to detect patterns and discrepancies which are otherwise difficult for humans to detect, fintech companies can identify fraudulent activities that may have otherwise gone unnoticed with the help of pattern recognition. This can help keep a check on new kinds of fraud or scams before they affect customers.
Investigative Fraud Analytics: Investigative analytics involves examining unknown patterns in complex datasets to uncover anomalies in transactions through the use of exploratory data analytic techniques, statistics, data mining, predictive analytics, and visualization. Examining fraud cases helps identify relationships between data and users, helping to detect potentially fraudulent behavior and uncover the link between perpetrators of fraud.
Benefits of Fraud Analytics
One of the biggest benefits of fraud analytics is the detection of suspicious behavior in real-time which helps in ensuring minimal impact on business or no/ minimal loss for customers. Also, it helps in formulating an effective strategy for detecting and preventing financial fraud as quickly as possible.
Fraud analytics centralizes scattered data by breaking down data silos and analyzing even the smallest bit of data, thus helping in identifying patterns that would have otherwise been ignored due to the scattered distribution of data. It has been proven to be effective in identifying complex fraud networks between individuals, organizations, and transactions through link analysis. From identifying fraud rings where a large number of criminals collude for committing a specific type of fraud like identity theft, or forgery to detection of synthetic Identities where criminals combine real and fake information to create a new identity for opening new accounts or making fraudulent purchases, fraud analytics examines the connections and associations in financial data and uncover collusion or organized fraud schemes.
To conclude, financial fraud is a complex problem that requires a multi-pronged strategy to detect and prevent it. In a fast-paced business environment, fraudsters are continuously developing new tactics to evade detection, making it challenging to keep up with the dynamic landscape of financial fraud. Fraud analytics allows fraud detection closer to the time of fraud occurrence and enables action in real-time, reducing the chances of significant financial losses. By implementing effective fraud analytics strategies, organizations can mitigate the risk of financial fraud and protect their reputation and assets, curb their losses, and maintain the trust of their customers and stakeholders.
(Ritesh Srivastava is the Chief Data Scientist at BharatPe.)
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