For the banking and finance business, fraud has quickly grown into a multibillion-dollar concern. According to a report by The Association of Certified Fraud Examiners (ACFE, 2018), fraud costs businesses about 5% of their annual sales.
To put this in context, the top five banks in the world generated sales of slightly over $1300 trillion in 2019, which equates to over $65 billion in fraud losses for these corporations alone, according to a business insider article (2019).
Fast fraud, in which hackers take advantage of flaws in digital fraud detection systems to steal customer assets, is a substantial contributor to the daily fraud losses that banks face.
New dangers are confronting how banks fight fraud in this digital banking era, as technology drives a rise in new channel offers and user interfaces to improve client experience.
Cybercriminals are employing a variety of strategies and tactics to take advantage of banks and force their customers to hand over their assets voluntarily.
Considerable growth in digital transactions has also created an appealing environment for fraudsters looking to camouflage their illegal operations among millions of daily transactions.
As a result of these developments, businesses must adopt innovative and competent technology to win the fight against online transaction frauds.
Given the size and scope of most of these vulnerable businesses, it has become essential for them to prevent these frauds or even foresee all suspicious acts ahead of time.
Frauds can be minor, such as non-payment for e-commerce orders, or serious, such as the public disclosure of consumers’ credit card information.
In this case, machine learning comes to the rescue. Organizations may considerably lower their risk of exposure to most of these frauds by automating data science operations with deep learning algorithms.
Approaches for Detecting Fraud that Currently in Use
Artificial intelligence is in usage for a long time for the detection and reduction of cyber frauds. Most businesses all over the world use Machine learning models.
A domain expert has two roles in most modern fraud detection methods (which use Machine Learning).
- They must collect transaction data from the past, and
- Assist with feature generation for traditional and sophisticated machine learning models.
The aforementioned characteristics are derived from raw data and are intended to be used in fraud detection.
An “incorrect zip code entered” could be a simple illustration of probable fraud.
Machine learning models are built based on these traits collected from previous data to detect or prevent fraud.
In the genuine sense, the procedure described above isn’t ideal. Here are some of the issues that make fraud detection more difficult:
Changing patterns of fraud across time
It’s difficult to combat fraudsters because they are looking for new ways to get around the systems and commit crimes. Deep learning models must have updates on the new patterns, model performance, and efficiency suffer. The machine learning models must be updated regularly or they will fail to achieve their goals.
Inequalities of Class
Only a small number of clients are attempting to defraud you. As a result, there is an imbalance in the classification of fraud detection models making them more difficult to develop. Because detecting the fraudsters frequently entails refusing some valid transactions, the result of this difficulty is a bad user experience for genuine clients.
Interpretations of Models
Because models often produce a score indicating whether a payment gateway challenge is likely to be fraudulent or not — without explaining why — this constraint is linked to the concept of explainability.
Feature creation can take a long time
The creation of a complete feature set by subject matter specialists might take a long time, slowing down the fraud detection process.
What is the best way to deal with these challenges?
Fortunately, there are numerous options for dealing with these issues and the challenges of electronic payment systems. Among them are –
The digital payment industry faces a lot of fraud. Ensemble modeling combines numerous models for a single job, such as fraud detection, to combat the ever-changing fraudulent tendencies. Combining traditional machine learning, deep learning, and linear models can capture a variety of fraud behaviors and maximize outcomes. An LSTM (Long Short Term Memory) deep learning model, can detect fraud in a sequence of events.
If a user signs in from a different city changes his street address on file and then purchases an expensive item on an e-commerce site, LSTM may flag the transaction as fraudulent. None of these incidents are indicative of fraud on their own, but the combination of all three is.
This method solves the online payment problems of classification imbalance while also reducing feature detection time. Humans aid the models by supplying information that helps them find new fraud patterns, traits, and dimensions. For example, in the above e-commerce use case, a human may deduce that such a sequence was fraudulent. The algorithm will then extrapolate this data and apply it to other scenarios, such as when users change their email addresses rather than their physical addresses. The algorithm learns from these instances based on human input, then identifies more from its learning.
The model interpretation difficulty can be solved by using AI to provide explanations for the approval or rejection of online payment security challenges. These advantages are provided by specific explainability techniques such as surrogate modeling, maximal activation analysis, and others.
For the financial industry, technology is a silver lining. It has completely changed the way people bank, whether they are using a smartphone or a tablet. However, this enhanced convenience has brought with it a slew of the new challenges facing payment providers, which banking staff must be able to solve quickly and effectively in real-time.
Fraudsters will continue to develop new methods of stealing assets from unwary clients; it is up to banks and customers to protect themselves from such attacks. We may not be able to win the war against fraud, but we can ensure that we are completely equipped to predict and stop fraudulent transaction conduct from severely damaging not only customer assets but also the organizations and brand names.
The journey ahead
Each of these resolution strategies improves the efficacy of machine learning models and reduces the number and severity of frauds. In other words, they’re necessary for applying both traditional and advanced machine learning to detect fraud.
Based on the developing ways fraudsters perform these illegal behaviors, obstacles for fraud detection. That is most likely to evolve and metamorphosize into hidden obstructions in the future. However, the top banking and finance software solution ensure that businesses’ fraud detection procedures evolve as well, reducing the impact of this crime.