BFSI simulation with big data analytics

Attributed to Catalytics Datum

A recent Deloitte survey identified that about 35 percent of BFSI respondents believed that future frauds would be detected using data analytics and other technology tools in the post-pandemic era. In the new normal, the realization has happened in every company of each sector to embrace advanced analytics supported by Big Data to automatically analyze online transaction activities and to identify criminal activity, and raise a Red flag to a potential threat.

Though, Financial Action Task Force (FATF) was established to avoid massive financial losses and instability within the economic ecosystem by imposing strict vigil on online trading, fraud schemes, smuggling, and illegal transfer of money. To control frauds, banks and consumers need to invest in anti-fraud Analytical technologies as a key to Fraud Risk Management.

Illegal activities via account fraud, credit card fraud, insurance fraud, illegal transfer of funds, scams generally occur outside of the normal range of financial and economic statistics. Scammers and organized crime syndicates have designed detailed schemes to drain millions of dollars through online transactions posing the greatest threat lulling over the BFSI domain.

For quite some time now, major banks have employed various AI processes for identifying cybersecurity applications, payment fraud, and frauds related to loans and customer onboarding. Different real-time anti fraudulent and Anti Money Laundering (AML) work to stop serial fraud and launders who moves their funds through transfers from one branch of a bank to another. The concept of In-Memory Computing (IMC) is used to improve big data analytics. Also through its fast and real-time data scaling features, it provides optimal customer experience. A robust AML is capable of real-time identifying unscrupulous changes within the financial system to arrest money laundering techniques and immediately end any fraudulent activity.

Anomaly Detection and Machine Learning Fraud Detection in Banking

\\For over a decade now, banks are using Anomaly detection, an AI technique for identifying inconsistencies and inaccuracies in payment and application information that have been raised from fraud automation, cybersecurity, and anti-money laundering processes. Anomaly fraud detection and prevention solutions require Machine Learning models trained on seamless continuous incoming data. This model has a baseline sense of normalcy for the inputs of banking transactions, loan applications, or information for opening a new account.

Moreover, Machine Learning based solutions are more beneficial to Banks in fraud detection as it is effective in more than one data channel to analyze, hence, it can detect fraud in more than one type of transaction or application simultaneously. The Machine Learning platform detects and flags any deviation from the norms resulting as “false positives” that triggers the alarm against fraud. In course of time, the percentage of the false positives significantly reduces as the model continues to learn.

This kind of baseline is used for interactions with various banking operations or entities. As fraud can come from merchants and account owners, their transaction information is used to train a machine learning model to recognize transaction processing properly. This involves pricing and omission of unpaid merchandise. By developing detailed risk profiles on customers and scoring them based on granular data banks can prevent fraud and illegal money transactions. Banks employ a risk scoring application that scrutinizes new account applications and accepts only the low-risk rate for fraud. This is done as the flow of data and APIS from the source are streamed into the Data Engine and then to deployment. Then the data is analyzed and transferred back to the data source. Thus banks can ensure only those applications are processed which are risk-free as the chief decision making Data engine does the onboarding processes for new customers, check their identities, eligibility and assess fraud risk of individual customers.

Another powerful method is Predictive and Prescriptive analytics solutions, effective for detecting fraud across multiple banking channels by analyzing data with a pre-trained algorithm to score a transaction on its fraud risk value. Machine learning models can also be used to develop predictive and prescriptive analytics software where predictions are made from the correlations of a predictive analytics engine and recommend the actions required once fraud is detected. Both predictive and prescriptive analytics requires the same data and training to implement. Banking data scientists label a high volume of transactions as fraudulent or legitimate then run it all through the machine learning model. Thus the machine learning model recognizes fraud methods used in fraudulent transactions. For example, the geographical location of the person who made a transaction may not line up with where the account owner was at the time of the transaction, also a fraudulent transaction may be for a product the account owner has never bought or would likely ever buy. The model can detect these inconsistencies after being trained; hence it is more sensitive to those data points within transactions and raises Red flag if the location data and the transaction are suspicious.

Banks normally have historical data labeled in their bank records. Fraud experts working on the machine learning model will identify and label the transactions that are fraudulent or not. The system gradually improves at discerning between fraud and legitimate banking operations with time along with more labeled transactions.

Predictive analytics-based fraud detection softwareis used by Banks to detect fraudulent multichannel payment processing. This involves recurring payments for financial services viz. financial advisory, eCommerce payments that involve a separate processing service.

Most of the time, eCommerce transactions, claims, loan applications, and financial services payments need to go through a third party payment processing system that the merchant has a partnership with. A bank using predictive analytics software identifies this as a separate entity, it then, compare the processing data for a given transaction with an established baseline for how that third party is supposed to process payments. As the payments are automatically charged and in the case of any decline, the account holder is notified and the payment is rejected. A fraud may attempt to tamper the billing information to a sinister account, which a predictive analytics application might be able to recognize.

Predictive Analytics protects Mobile Banking and eCommerce apps from fraud as it can detect fraud in mobile apps for banking or remote ordering and payment for merchandise. The ever-updating Smartphone apps pose a challenge but predictive analytics can also detect anomalous user behavior within these apps viz. logging into an account from a phone not belonging to the account owner. This can be identified with the smartphone geolocational data along with the customer’s personal data stored in the device.

Big Data Analytics thus not only makes the BFSI domain safe but ensures smooth and hassle-free business operations with real-time detection and arresting frauds and money laundering. Real-time data integration and streaming analytics need to be embraced by Insurance and banking companies to deliver a better customer experience along with increased profitability. As global transactions are monitored by regulatory compliance to respond to customer queries immediately regarding detection of fraud and money laundering.

Thus, being an end to end solution for real-time data integration, Big Data Analytics offers data visualization, streamlining analytics to enable automated response to stop criminal acts such as misappropriation of funds, extortion or trafficking. Global BFSI leaders have empowered themselves with the right AI-enabled fraud detection software, assess where AI can fit in their fraud detection workflows, and increase the success rate of their fraud detection technology initiatives to mitigate financial and regulatory risk, decrease false positives, and reduce overhead costs to ensure an AI enabled successful banking processes as they embark in the journey to the future.