How ML is solving fraud issues in finances

By Srikanth
6 Min Read
How ML is solving fraud issues in finances

In today’s digital world we all use certain finance apps in our lives. The day to day transactions depend largely on the finance apps on our mobile phones through which our livelihood depends.


Scams and frauds related to finance happens with the aim to hack bank accounts and get huge money and also important information. The banking frauds it’s not a new thing, this has been present for the longest time, but now with our phones and internet medium it has become more widespread. With the widespread of the business the increase of frauds and scams in finance has increase to double folds. With various websites and apps being the soft targets of many.

How ML can help in fraud detection

However, the evolution of technology has provided businesses with new tools to combat fraud. The rise of new technologies like  Artificial Intelligence, ML, Cloud Computing, Edge Computing, and Deep Learning have come in handy and helped the finance sectors to make businesses theft and scam proof to a large extent. With the aid of these technologies, businesses can detect and respond to fraud in real-time, thereby minimizing losses and protecting their customers.

The integration of digital platforms in the finance industry has led to an increase in fraudulent activities, posing a significant challenge to the industry and its users. In response to this, financial institutions are leveraging ML to detect and prevent financial fraud.

Machine Learning has become an essential tool in the finance industry to protect customers from criminal activities that seek to acquire money through deception. Financial institutions are investing in building robust solutions that provide optimum security to their customers, and machine learning is a critical component of this process. Mobile app developers are also actively integrating various algorithms and explicit programming to create fraud-free apps for financial institutions. This proactive approach has helped to minimize fraudulent activities in the finance industry, safeguarding the financial interests of users.

Prime targets when ML needs to focus

The finance sector is unfortunately a prime target for scams due to the large sums of money involved. Some of the various types of scams caught in the finance sector include:

•       Ponzi schemes: A fraudulent investment scheme where returns are paid to earlier investors using the capital of newer investors.

•       Identity theft: The illegal way of  using a bank account or other banking information to hack into their accounts and take money.

•       Phishing scams: Various notorious ways through which the scams happen wherein  they try to collect the private information like password, credit card details through online or mobile SMS.

•       Fake investment opportunities: Offers for investments that promise high returns but are actually non-existent or fraudulent.

•       Credit card fraud: The unauthorized use of someone else’s credit card or credit card information to make purchases or withdraw funds.

•       Money laundering: The process of concealing the origin, ownership, or destination of illegally obtained money by disguising it as legitimate funds.

•       Mortgage fraud: Fraudulent activities related to mortgage applications, such as falsifying information on an application or appraisal.

•       Insider trading: The illegal use of non-public information to make financial trades or investments.

•       Advance fee scams: Scams that require the victim to pay a fee in advance for a promised financial gain or reward that never materializes.

•       Cyberattacks: The use of malware, ransomware, or other malicious software to hack and access to financial data and  records which becomes a huge loss .

Machine learning can be a valuable tool in preventing banking scams.

Here are some ways that it can be used:

Fraud detection: ML tools will help to  identify patterns in financial transactions that can lead to fraud.

Risk assessment: ML algorithms can be used to monitor the risk of how risky a particular  transaction can be based on the transaction history. It can also asses what mode and how much amount the particular account can be allowed. 

Customer authentication: Machine learning algorithms do have tools through which they can biometric, typing patterns, typo errors which can lead to fraud alerts.

Anti-money laundering: Machine learning will be used to audit large amounts of financial data and identify malicious activity that may be indicative of money laundering. This can help finance companies  comply with regulatory requirements and prevent criminal activity.

Cybersecurity:ML algorithms can be used to identify patterns in network traffic and detect anomalies that may be indicative of a cyber attack. This can help prevent data breaches and protect customer information.

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