The digitization of banks has created a revolution in the consumer-bank relationship, and there seems to be a steep rise in banking fraud. Presently, digital banking fraud has become an industry using sophisticated tools and often conspiring with dishonest bank staff to embezzle huge sums from personal accounts. Such frauds have pushed up the banks’ liabilities since they have to cover the losses their customers face from fraud. As the FinTech industry grows faster, digital channels have multiplied, and so have the fraudsters.
The tools that help banks alert the upcoming digital banking fraud understand weaknesses and employ the latest best practices, so they can not only catch but also prevent it. With the new digital technologies coming up for the banks, fraudsters take benefit of security loopholes that go undetected, and the banks end up with a substantial fine or data loss.
On the other hand, digitizing banking services also gives new technological solutions to tackle security challenges and notice questionable conduct, enabling banks to protect digital data from fraud.
Table of Contents
Five Types of Banking Fraud and How Digital Transformation Can Help
1. Money Laundering and Screening of Sanctions
Money laundering has been a leading compliance fine source for financial institutions. Banks use smart transaction tools to track money laundering attempts and avoid being fined. By reducing the number of false positive and negative alerts, banks spend less on catching culprits.
Banks can track money laundering and screen sanctions using artificial intelligence (AI), like the robust anti-money laundering (AML) process. Banks use AI solutions to scrutinize movements from a global viewpoint and blacklist bad actors without having to compromise with regulatory compliance guidelines. This is blockchain, and Microsoft, Blue Prism, and Identitii recently used blockchain technology to address the money-laundering problem.
Success Tale: Jaima Co-operative Bank uses a system called BankingEasy, run on Microsoft Azure, that allows the organization to benefit from the sophisticated AML suite to handle digital data.
2. Internal Fraud
Corrupt employees are more of a liability than anything else. The Association of Certified Fraud Examiners disclosed that the median loss from a case of occupational fraud globally amounts to $150,000.
Solutions that use AI or machine learning algorithms can analyze extensive data warehouses and mark patterns that solve workplace fraud by identifying all deceitful expenses.
Success Tale: Willis Towers Watson used multifactor authentication systems to control employee spending that tracks internal fraud. Employees can access information only from within the corporate network, and the organization can monitor individual and group spending with Azure Resource Manager.
3. Credit Card Fraud
Credit card fraud is the most common, but new tools of AI with machine learning algorithms manage the risk and solve the problems to some extent.
Earlier, financial organizations used linear algorithms to identify good from dubious transactions. Today, banks make use of AI with machine learning algorithms that isolate good from fraudulent dealings. Banks can use these algorithms because of AI-driven tools with the computing ability of cloud technologies.
Success Tale: With advanced algorithms, banks also use biometric transactions. First Tech Federal Credit Union shifted its operations to the cloud and became the first credit union that participates in the MasterCard biometric payment model. The new technology of “selfie-pay” doesn’t need a credit card for a transaction, significantly reducing the risk of credit card fraud.
4. Mobile Fraud
With the growth of mobile banking services, fraud attempts using a mobile device have multiplied.
Fraudsters consistently devise new fraud methods to do fraud. As per Guardian Analytics, 72\% of mobile banking fraud is executed via mobile remote deposit capture (RDC). RDC allows images of remotely scanned checks to a bank for deposit via an encrypted Internet connection.
Yet, financial institutions use global mobile devices to authenticate customers and employees.
Success Tale: Türkiye Finans Bank uses solutions for data security like Azure Multi-Factor Authentication. It provides the employees with a second level of identity authentication on top of username and password when accessing corporate applications through the Internet. Even if fraud happens using the username and password, it will not have access to corporate data.
5. Identity and Social Fraud
Bank fraud is steeply rising as criminals use more advanced digital methods. AI with machine learning has offered customers cutting-edge solutions for protecting their identity and securing safe access to banking services.
AI and machine learning that uses deep learning algorithms help financial institutions create advanced systems that can match DNA sequences. It assists identity recognition in many ways, from digital signatures to biometric data identification and selfie pay.
Enhancing technology tools enables criminal gangs to execute more complex frauds; a technology-based system is the only way for banks to protect their brand stature and customer confidence.
Eight critical ways in banks’ fight against fraud:
Promptness: Anti-fraud systems catch potential fraud case and blocks suspicious transactions as they pass through the bank’s systems. Technology developed to fight fraud through non-digital channels does not work well against digital banking fraud since its execution is not prompt.
Exhaustive: A technology-based approach helps the bank scrutinize every transaction in its system, which seems impossible for humans. As digitization grows, technology equips it with scalable ways to respond.
Risk Sensitive: At times, genuine consumers do transactions outside their regular behavior pattern. AI-driven fraud detection systems help the bank to assess the risks of blocking legitimate but irregular transactions.
Customer-Centric: To effectively combat imposter threats, banks need to judge when an imposter uses actual identity data to carry out fraudulent transactions.
Full Circle Watch: Banks, even consumers, face the danger of fraud. Conspiracy between criminal gangs and bank staff has become a persistent problem. AI coupled with deep machine learning enables banks to monitor customers and staff through a single dashboard. Full circle watch helps protect the bank against complicated frauds concerning internal and external actors.
Efficiency: With advanced technology as the first security line, banks can better use their employees’ time and skills. It focuses on analyzing and verifying suspect cases identified by the anti-fraud system.
Comprehensive Record: Automated fraud detection systems deliver complete audit tracks and promote proper record-keeping. It helps the bank comply with regulations.
Capacity to Learn: Banks must learn each customer’s set patterns of behavior so that any irregular transaction makes sense to the banker. Technology offers the most helpful way to watch transactions and notice peculiarities in this way.
Conclusion
1. Cyber fraud started with some hackers stealing modest sums and has become a global unlawful industry of skilled criminals with banking knowledge and access to exceptionally advanced technology tools.
2. Patterns of fraud have become more complex involving more people colluding with criminal gangs and individuals inside the bank.
3. The more advanced cyber fraud becomes, the greater the risk that it will fool the AI systems that banks deploy to catch fraudsters.
4. Open Banking, as per EU’s second Payment Services Directive (PSD2), has allowed banks direct access to their customers’ personal banking data via APIs. Open Banking has also provided new chances for fraudsters to access customer data.
5. The most advanced anti-fraud system in the market today is Big Data technology, allowing banks to detect and block suspicious activity as it occurs.
6. Improved technology tools based on machine learning give banks access to cutting-edge anti-fraud systems that are more efficient and less pushy for customers. The banks that execute them can anticipate fewer false positives, less fraud, better clientele, and fewer compliance fines.
