The average thief isn’t on the streets or in train stations anymore; but is online. Due to its surge in popularity, and fast transaction cycles, online lending is a prime target for cyber criminals. Though financial institutions may have always had customer security in mind, the industry has felt the backlash in recent years.
Yet, as the saying goes, knowledge is power. You can make the best utilization of the convenience provided by technology aiming yourself with the power of knowledge. Cyber-attack methods and tools keep evolving with advancements in technology, increasing the possibility of ingenious scams that can be deployed from anywhere across the world.
Role of AI in combating fraud
In this scenario of increased cyberattack, AI mechanisms are emerging as the means to strengthen cybersecurity and thwart attacks. Research reveals that 63% of financial institutions believe that AI can prevent fraud, while 80% agree that AI plays a critical role in reducing fraudulent payments and attempts to commit fraud. Machine learning technology can be deployed across multiple channels (e.g. transactions, loan applications, etc.) in the financial industry. Banks and financial institutions can benefit from patterns that emerge with use of AI and ML to prevent frauds even before they happen.
Leading ways lenders are using AI for fraud detection
Building purchase profiles: To accurately detect fraud, financial institutions must first understand what typical customer behavior looks like. Using machine learning to sort through vast amounts of data from past financial and non-financial transactions, FIs can build and slot customers into several different profiles.
Develop fraud scores: All transactions can be assigned a fraud score by using data from past legitimate transactions, incidences of fraud and risk parameters set by the financial institution. The score, which considers variables such as transaction amount, time, card use frequency, IP address of a purchase, and much more, is used to assesses the fraud risk involved with that particular transaction.
Enhance underwriting: AI can have far-reaching benefits for underwriting performance. Increasingly accurate loss predictions enable underwriting teams to spot good and bad risks, grow a profitable portfolio, and automate processes to streamline their workflow.
Fraud investigation: Machine learning algorithms can analyze hundreds of thousands of transactions per second. Investigating and prosecuting fraud claims can be incredibly time-consuming, so ensuring agents are armed with the proper tools to increase efficiency is essential.
Know Your Customer (KYC): AI-backed KYC measures can verify ID and documentation, match fingerprints and even perform facial recognition almost instantaneously. This powerful tool strikes the right balance between customer security and convenience.
Digital organizations can identify automated and more complex fraud attempts faster and more accurately by combining supervised and unsupervised machine learning as part of a larger Artificial Intelligence (AI) fraud detection strategy. There is no question AI is making cybersecurity systems smarter. Whether this technology is used for securing authentication, threat detection or bot battling, AI and ML can prevent bad actors from infiltrating and manipulating company networks.
Insight Consultants fraud detection strategy using ML
To detect fraud,
- Machine learning model collects data
- Model analyzes all the data gathered, segments, and extracts the required features from it.
- Predict the probability of fraud with high accuracy
An outdated financial system is always full of loopholes tricksters can use. Luckily, machine learning has the potential to improve bank fraud detection with data analytics and help nearly every industry.
If you are looking for ways to harness the power of machine learning and AI for your business, or would just like to know more, Contact Us.
