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Top Use Cases From Data Analytics and ML for Effective Fraud Detection and Prevention

by Rajesh Shashikant Renukdas, on May 19, 2021 5:45:32 PM

While evolving technologies have ushered an era of accessibility and ultimate convenience, it has also led to an exponential surge in new unique patterns in acts of monetary thefts, tax evasions, dark-net transactions, funding of illegal activities, etc. Moreover, traditional fraud detection and prevention systems rely heavily on the data sets at hand that can detect known patterns of fraud but are incapable of detecting new, powerful, and visually disguised frauds. These frauds leverage loopholes in the digital business systems and employ new and advanced technologies in parallel to commit these frauds. As a result, fraud detection has paved the way to become a trillion-dollar business. The Association of Certified Fraud Executives in USA or ACFE reports that businesses lose around 5% of revenue to fraud every year, which when measured on a global level, translates to $4 trillion a year.

pocketing-company-money-cropped-shot-of-a-business

Clearly, there is an imminent exigency on a business level to shore up the security layer with the help of next-gen, cognitive, and smart technologies, on not just systems deployed on-site but through apps that can remotely connect to these systems and be operated on the go. In this blog, we will look at some riveting use cases of AI, ML, and other high-end technologies to detect and prevent fraud. Let us begin by looking at how a fraud detection and prevention system works:



How does fraud detection and prevention software work?

Fraud detection and prevention software can handle massive quantities of data from a system and AI/ML can help it learn what's normal within a collection of data sets. The software can directly fetch business rules or machine-learning algorithms from the cloud to spot incoming and existing anomalies, assign risk scores to transactions that may lead to repercussions recorded in the past, and send push notifications to the authorities, who can manually choose to halt the respective transactions. These tools can be used by businesses of all verticals, from banking and e-commerce to government and private entities to constantly monitor and exercise prevention for potential fraudulent actions made by their employees, customers, or an external body. Here are some industries that can leverage the power of fraud detection systems:

Tax Fraud

Widespread data breaches have made it easier for fraudsters to obtain real information from governmental organizations and use it for tax fraud. Predictive analytics can reliably assess individual tax returns and figure out what recent actions of the taxpayer have deviated from the characteristics of all his/her previous returns, aligning them with their most recent paperwork. The systems can track cluster data from on-premise systems to identify and store elements that may be common to numerous returns and other elements that may or may not signal an illegitimate activity. These data sets can then be mapped with data collected from external entities like employers, real estate builders, hospitals, and insurance companies, to identify the gaps and detect pre-enlisted fraudsters.

Pharmaceutical Fraud 

Fraud in the medical sector can happen when a provider prescribes a drug or other treatment to someone who doesn't have a genuine medical need for it, or if a drug company charges inflated prices for medicines and creates bills for services or products never sold. In 2019, a drug manufacturer had to pay $2.2 million to the state of Washington after it reportedly purposely delayed the Food and Drug Administration from approving generic versions of the drug so the pharmaceutical company could remain in control of its pricing. These cases that rely entirely on whistleblowers can be detected by examining the approval timelines for similar generic drugs and contrasting them with a medication awaiting approval. AI/ML-based systems can be deployed on federal drug approval authority sites. Data analytics could also help by enabling corroboration of services rendered on the provider side with services received on the consumer side, by analyzing data on lodging, bank transactions, and accounts receivable.

Ecommerce fraud

eCommerce fraud costs businesses billions in chargebacks, overhead, and unnecessarily declined orders for loyal customers. A system can be trained to use powerful machine-learning algorithms at the point of sale to instantly recognize good customers and take actions on consumers who consistently demand returns, file false damages or order certain harmful products without any imminent business need. These systems can be integrated with devices that can instantly alert authorities of abnormal activities, who can then inform the respective legal or government body to eliminate any liabilities. Merchants can safely approve more orders, expand internationally and eliminate the costs of fraud while providing a frictionless customer experience to legitimate customers.

Credit Card and Bank Fraud

Banks are most prone to frauds and therefore mandated to deploy a task force to inquire about a suspicious activity manually when a data analytics system triggers it. Well-trained data analytics systems can look for probable issues 24/7, and spot illegal activity in different time zones, activating automated communications through emails, pre-recorded calls, or push notifications. Moreover, an system allows for prompt responses to suspected wrongdoing, limiting the problems caused by a fraudster. Leading retailers – like Walmart, Stop & Shop, and Home Depot – are enhancing their payment and fraud detection systems, using artificial intelligence that learns transaction norms and infers risk from the context of each transaction.

Features of a Fraud Detection and Prevention Software

A fraud detection and prevention system is the core of any fraud risk management strategy. Here are some of the mandatory features that it must include:

  • Real-time transaction screening and review automation. Fraud detection software with ML or rule-based capabilities should constantly monitor incoming data in real-time, conduct an automated review of most of the orders themselves. 
  • Real-time as well as batch integration of data.
  • Comprehensive modules for workflow auditing and case management.
  • High-performance testing tools for scenarios
  • Reporting for investigations and operational performance
  • Push notifications to send alerts about halted transactions in real-time.
  • Dashboards to monitor key performance indicators in real-time, for instance, track orders and learn about their status and additional information like payment method, location, channel, etc. 
  • Reporting capabilities on suspicious activity or a total number of transactions.
  • Visualizations of basic industry-specific fraud patterns to let stakeholders better understand interconnections between user behavior and fraud attempts.

Conclusion

The use cases and advantages of proprietary software for fraud detection and prevention tailored to an organization’s needs can never be fulfilled through a traditional subscription-based fraud detection system that may or may not comply with an organization’s legal or international standard. Businesses operating in a high-risk environment involving huge volumes of data need an effective solution that can track parameters of relevance such as customer’s activity history, location, tracking and validating IP address, and ensuring that transaction data doesn’t exist in the global and merchant-specific fraud blacklists. This blog enlists just a few of many aspects of fraud detection, however, a successful fraud detection strategy is a long haul that involves collaboration with experts in the technology field. To get started with a solution for fraud detection, get in touch with a professional fraud detection and prevention software development services company.

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Topics:Machine LearningFraud Detection

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