According to IBM, the world will have lost about 44 billion dollars due to fraud by the year 2025. With the advent of better technology, cyber crooks too have better methodologies to perform illegal activities and financial technology is one of the hardest hit domains.
In this blog post, we will explore the common types of fraud that occur in fintech and how artificial intelligence can help.
Common Types of Fraud
These are incidents in which a particular person, without having the goodwill of paying back, makes a deal for receiving goods or services in the retail market. In a banking situation, they may put in an application for a loan, but misrepresent their financial condition in order to receive a larger sum of money or a lower rate of interest.
Thefts that include impersonating another individual. This may include illegal use of another person’s bank details, credit card pin, or any other personal credentials to avail services, make purchases or use them to open a new account, get a new credit card, etc. This necessitates the development of reliable fraud detection in banking.
A bot attack comprises a set of automated scripts which disrupt the working of a website or cause fraudulent activities. They may begin as spam and go onto large crimes branching into a variety of illegal activities.
Imposter scams are those who pretend to be the bank or financial company representatives, offering a service. To avail that they ask for the customer’s financial credentials and bank details like credit card number, OTP, etc, and go on to misuse those details to rob them or scam them. So it is important to identify credit card fraud detection
Deep Fake technology makes use of a collection of images, videos, and corresponding audio to generate resemblance to a person who isn’t there and makes it seem like they are performing actions or speaking words that they aren’t in reality. Nowadays, the same technology can be utilized to counter such illegal activities through deep learning fraud detection.
Buy Now, Pay Later
This can be used to execute fraudulent activities by using expired credit cards or information obtained through phishing to buy products with the buy now pay later scheme of events, but not paying later on.
The old way of identifying fraud was to use computers to examine a large amount of structured data against a set of rules. Because fraud generally consists of several instances or episodes involving recurrent breaches utilizing the same approach, this strategy necessitates complex and time-consuming investigations. But with artificial intelligence and machine learning taking the modern world by storm in recent years, they have effectively entered and revolutionized a wide range of fields, including the fintech business.
Several financial services companies are progressively incorporating AI and machine learning fraud detection into their ecosystems to take advantage of the massive amounts of data available through newly acquired digital channels. Furthermore, the pandemic has accelerated its spread, prompting two-thirds of financial services organizations to implement the technology into their operations.
The Benefits of Using Machine Learning in Fraud Detection
Working with a Large Amount of Data: While financial institutions collect large amounts of unstructured customer data that has little bearing on decision-making, artificial intelligence transforms this data into easily actionable insights. Artificial intelligence (AI)-driven systems evaluate consumer data and identify functional patterns that help to drive real-time decision-making processes for transactional fraud detection.
These AI-powered applications use traditional data as well as other data such as transactions, social media activity, and work history with the borrower’s approval. Lenders can use this alternative data to create personalized credit solutions based on the borrower’s present financial status and anticipated future needs. Over time, AI-powered learning algorithms refine and improve their data characteristics and capacity to sort and label data, allowing them to deliver accurate borrowers’ profiles and future creditworthiness, reducing fraud risks.
Adaptive Analytics: Adaptive analytics improves the likelihood of detecting fraudulent activity by making tiny alterations based on the most recent confirmed case dispositions, allowing for a clear differentiation between non-frauds and frauds. These systems update traditional neural network fraud models on a regular basis in response to real-time fraud tendencies. Its ability to study and learn from current fraud trends assists financial institutions in detecting, forecasting, and blocking future fraudulent activities.
Fraud Detection in Real Time: The exponential growth of real-time transactions has overwhelmed traditional rule-based fraud detection technologies, resulting in false declines at both online and offline touch points. Financial organizations can use machine learning algorithms to find previously unknown patterns that would be impossible to follow using traditional fraud detection approaches.
These patterns are maintained in real-time within the system in order to detect future fraudulent activity, thus acting as a self-improvement security mechanism. Businesses can use machine learning to correctly determine the validity of payments and identify transaction fraud detection in milliseconds.
The Benefits of Using AI in Fraud Detection
● AI is redefining fraud prevention by taking into consideration new activities, habits, and patterns in transaction anomalies rather than relying completely on prior experiences. Previously, fraud protection systems relied solely on rules that evaluated past fraud trends but offered no predictions for the future.
Digital firms can now acquire better clarity about the relative risk of consumers’ activities by integrating supervised learning algorithms developed using historical data with unsupervised learning. Artificial intelligence has made it feasible to make decisions such as whether to accept or reject a purchase request and helps take better actions to eliminate fraudulent activity and reduce chargebacks and risk.
● Rather than waiting for weeks to initiate chargebacks, AI allows fraud attacks to be detected in real-time. With the ability of AI to detect fraud attacks in less than a second, using powerful AI-based grading tools, it eliminates the need to constantly play catch-up to online fraud by balancing supervised and unsupervised learning.
Also, fraud analysts can use AI-based fraud protection to get real-time risk scores and better insight into where to establish threshold scores to increase sales and reduce fraud losses. AI provides a fraud analyst with a wholesome picture of transactions, including historical information in context. By employing unsupervised machine learning to spot anomalies and get insights into activities, fraud analysts may instantaneously confirm or redefine their threshold levels, effectively minimizing risk.
● AI has now made it possible to take action on more sophisticated, complex, and subtly executed attacks. One of the conundrums that fraud analysts face is determining the appropriate threshold for the decrease rate. Instead of guessing, fraud analysts can use AI-based scoring algorithms that incorporate the benefits of both supervised and unsupervised learning.
Artificial intelligence-based fraud ratings reduce false positives, which are a key source of customer annoyance. All of this correlates to fewer manual escalations and declines, as well as a better overall customer experience.
The Bottom Line
Unfortunately, even with the most advanced tools and processes, criminals are often one step ahead. Though AI will not be able to eliminate all sorts of fraud, particularly those stemming from internal corruption, it will undoubtedly help to minimize and prevent total fraud.
About the author: Abhilash Dasari has over 7 years of experience working with SaaS startups defining and implementing Growth Strategies. Abhilash is currently heading Product at RecoSense and involved in planning, coordination, and development of various products, marketing, and sales deliverables, including website content, blogs, decks, data sheets, landing pages, emailers, research reports, and feature insights to grow demand and revenue.