Digital fraud is the banking sector’s primary challenge, leading to immense losses every year. As per McAfee’s reports, cyber fraud currently damages the economy by USD 600 billion of GDP on a global basis.
Banking industry frauds are categorised into corporate banking scams and non-corporate e-banking frauds. Corporate banking scams are more challenging as they affect banking systems and the economy. Such scams primarily occur with bank loans and the misuse of banking access.
On the other hand, non-corporate e-banking frauds are individual-centric such as stolen banking passwords, stolen PIN details, or cloning of debit/credit cards. We need to prevent these financial frauds by adopting advanced technologies.
What Is Identity Theft Fraud Prevention?
Identity theft protection is a budding industry domain that monitors and tracks people’s credit reports, financial transactions, and banking activities. On the other hand, identity theft fraud prevention lets you take self-protective actions and trim down the overall fraud damages.
However, identity thefts can happen despite your safety measures. So, you need to modernise and automate your fraud detection approaches.
Common Types of Banking Fraud
- Identity theft – Scammers steal your personal or finanical information to carry out unlawful transactions like applying for loans in your name. You are then responsible for repaying a debt that fraudsters obtained in your name.
- Card Skimming – Card skimming is a form of bank fraud in which fraudsters utilise a tiny electronic tool called a skimmer to acquire card information. The POS device or ATM typically has the skimmer installed. When a card is passed through a skimmer, the tool reads the magnetic strip on your credit or debit card and records important data. Fraudsters use a blank card with this information copied on the magnetic strip to withdraw funds from your bank account.
- Phishing – It is a method of ‘fishing’ for your banking information. Phishing can involve an email that appears to be from a well-known organisation, such as a bank, or a well-known website. This information is then used by scammers to withdraw your money or make any transactions. Please be aware that banks will never request private information like a password for a transaction or login, an OTP, or anything similar.
Significant Role of AI in Fraud Detection across the Banking & Financial Industry
What is Artificial Intelligence and AI Full Form? By definition, AI or Artificial intelligence makes it possible for robots or machines to acquire knowledge from experience, fine-tune to fresh inputs and execute human-like tasks through automation.
AI in the finance domain plays a vital role in the current scenario. The more financial data AI has to work with, the more in-depth analytics and insights the financial industry can get from their AI technology.
Already, we are seeing the crucial role of Artificial Intelligence in the Banking sector that assists with fraud detection from a progressive viewpoint instead of waiting for frauds to occur and then act. Artificial Intelligence technology is highly result-oriented in detecting scams, with 63 percent of financial institutions conveying that AI is proficient in avoiding cyber fraud before it happens.
Strategies for fraud detection and prevention in banking using AI
There are multiple ways AI can be used to detect and prevent fraud in the banking industry. With such powerful tools at our disposal, the possibilities are endless:
- Using behavioral analytics
Behavioral analytics can be used to analyze patterns of concerning behavior in users. Habits of merchants, accounts and even devices can be tracked and measured. Due to false positives and other annoyances, transactions might be abandoned because of inaccurate information provided by user profiles.
- Using self learning AI
An AI that learns on it’s own is an unstoppable force. Due to the increasing frequency and difficulty of fraud attacks, a self learning AI seems like the best choice. Adaptive analytics can be used to enhance the security of user profiles. Adopting adaptive analytics solutions increases the sensitivity of fraud detection systems to changing fraud patterns by automatically adapting to recently proved case dispositions.
- Using supervised and unsupervised AI models together for safeguarding
The most common sort of machine learning is supervised learning, which is based on a large number of accurately “classified” transactions. Fraud or non-fraud is attributed to every transaction. The models are trained by ingesting large amounts of labeled transaction data in order to uncover patterns that best indicate legitimate operations. The accuracy of a supervised model is directly related to the amount of clean, relevant training data utilized in its creation. When labeled transaction data is sparse or non-existent, unsupervised models are used to detect anomalous activity. The patterns in the data that standard analytics miss must be uncovered using self-learning in these situations.
Significant Role of Machine Learning (ML) in Cyber Scam Detection
Machine learning or ML is a substantial area of AI that keeps a machine’s in-built algorithms current irrespective of alterations in the global economy.
Exploring current security systems, even advanced analytics software systems are principally reliant on humans to evaluate data and detect suspicious activities. This dependency is inclined to problems such as slow speed and human faults. The applications of machine learning can resolve most of these legacy challenges.
Significant benefits of machine learning and AI for banks include swiftness to evaluate massive data sets, detect subtle alterations in patterns of banking information, and spot fraud quicker. So, machine learning solutions have enhanced accuracy levels for cyber fraud detection.
Fraud Detection through AI and ML for Different Banking Applications
Applying AI and ML technologies to fraud detection lets banks detect actual financial transactions vs fake transactions on a real-time basis and with better precision levels.
AI can assist in combating application scams by sensing illegal activities early in banking transaction processes. Algorithms can explore links between financial applications for loan and credit card applications and even track freshly opened accounts to stop monetary damages before it occurs.
AI can ensure that payments are being made readily by an individual. The technology can also trim down false positives with conventional fraud detection techniques.
AI can track spending and deposit patterns with time and notify employees of glitches, and block payments before execution. Algorithms can pull from various data points, from transaction initiation to the endpoint, to check nonconformities from standard patterns.
Not merely cybercriminals are involved across mortgage frauds, but even industry insiders such as bank officers, financial brokers, and other connected professionals are involved in scams. These activities by staff members are typically to commit frauds for profitability. An individual illegally uses the mortgage lending procedure to steal money from homeowners and involved lenders.
One of the challenges banks face is that frauds can take many structures, shapes, and methods. For instance, research showcases that loan frauds are the most expensive forms of identity theft, averaging around USD 4,687 per occurrence.
The concept behind leveraging artificial intelligence and machine learning is that fraudulent transactions showcase some firm patterns separate from authentic ones to detect these activities.
AI and ML are the Future for Fraud Detection in Banking Sector
So, with banking frauds leading to increasing fraud losses to customers and banks each year, it is significant to take swift measures to fraud risk management and irregularity detection.
Whereas humans and rules-based programmed solutions can knowingly or unknowingly oversee transaction details, you can train AI and ML algorithms to scrutinise even the most unconnected data sets to find an explicit information pattern.
Hence, the outmoded fraud detection solutions are not adequate anymore. Leveraging Artificial Intelligence (AI) and Machine Learning (ML) is swifter, more effective, and more precise than rules-based solutions.
The advanced fraud detection tools powered by AI and ML save time, effort, and funds. These technology programs are now the future for those who want to stay updated, competitive, and prominently fraud-free.
How HyperVerge is Enabling AI to Banking Industry
The financial technology product built by HyperVerge, the Palo Alto headquartered company, is crucial. It assists financial and banking institutions in delivering continual services and saving the cost and time spent on labour-intensive processing.
With AI solutions powered by HyperVerge, the company enables deep-learning networks to power applications for its enterprise clientele in banking and financial services.
Leveraging the HyperVerge Fintech Platform, financial companies can use AI to go from lead to customer in less than 5 minutes. The platform dedupes fraud checks, controls transactional frauds, stops customer & agent frauds, and helps in revealing legacy frauds.
Banks can gain from quick time to value by taking a more practical approach to remaining ahead of fraudsters through Artificial Intelligence (AI) and Machine Learning (ML) solutions.
Discover how HyperVerge brings AI to the banking sector by early detection and actionable outcomes by its AI-powered product. Would you like more details about the product and how you can set it in your current workflow? If so, write to us at contact@hyperverge.co. Else quickly fill out our request form right here.
