Artificial Intelligence is becoming increasingly popular in the Finance Industry with recent advances in technology. Be it dealing with public relations or making an investment decision, AI sub-branch machine learning is creating better opportunities for financial institutions. With machine learning, Fintechs can make their financial operations smooth and secure and provide the customer with an effortless experience. According to Cision PR Newswire, the total revenue generated by integrating AI products in the Finance sector will be around $7,305 million US dollars.
How Much Machine Learning Is Useful?
Machine Learning (ML) helps in identifying various patterns in big data sets. By detecting relations between events and sequences, ML algorithms extract the relevant information hidden in raw data acquired from different sources. These patterns are hard to identify with human expertise since there exists a certain level of complexity, and the amount of work is enormous. Artificially intelligent systems can learn from a set of examples and perform predictions based on learning, which helps FinTech businesses to recognize potential opportunities ahead of time. Artificial Intelligence also utilizes biometric authentication technology to make customer verification procedures safe, reliable, and fast for Fintechs.
Machine Learning Applications in the Banking Industry
With the power of machine learning, Fintech companies operating in the banking sector can achieve better interest rates from ventures. According to research by Venture Tech on categories in Artificial Intelligence, machine learning technologies and applications dominate the finance sector by a fair margin. The fact that the banking and finance industry incorporates massive volumes of user data makes the problem challenging. Machine learning presents both a viable and reliable means to process and analyze the information through real-time algorithms with better turnaround times.
Optimized Loan Management
There were days when issuing a loan from the bank took long hours of verifications and lengthy procedures. With advances in the recent past, peer-to-peer lending over the internet made it possible for consumers to borrow loans over the internet much faster. But recently, interest rates have also gone sky-high for P2P lending that forces conventional banks and digital lenders to improve the market situation without adding any potential risks.
Machine learning beats conventional ways and means of loan accommodation by better assessing the borrower’s ability to pay back funds. This is possible by analyzing the user’s portfolio through a series of verification checks which involve machine learning techniques. A few factors which these algorithms take into account are social profiles, rent payments, or utility bills.
Conventional Loan Grants
Banks and financial institutions have been using antiquated methods to grant loans to borrowers. Banks review loan applications based on the following information: credit history, cash flow statements if you are an organization, previous bank statements, and proof of bank security. Based on these items, the bank allotted a particular credit score to the individual requesting the loan. The review process becomes tedious and time-taking when performed by a human workforce.
How does Artificial Intelligence Help?
AI-based algorithms assess the complex patterns involved in financial records to assign a risk score based on each parameter. An accurate risk score is computed by aggregating and analyzing data of thousands of users, which is then used as ground truth for further computations. Machine learning algorithms embedded in banking systems review the lender’s information based on pre-defined parameters, generate a risk score based on the analysis and approve the lender’s request if the score is below the threshold and vice versa.
Benefits and Takeaways
Credit scoring for loan grants is automated and easy to perform with intelligent algorithms and come with a couple of advantages which are:
- More borrowers are attracted towards banks and lending platforms with better loan approvals
- The overall risk of a fake lender is significantly mitigated, and an accurate credit score is assigned
Identity Theft Protection
Fintechs face many cybersecurity threats due to a large number of monetary operations involved in their business activities. Whether it is an already established enterprise or an emerging startup, cybercrime is always imminent. The aftermath of a data breach can cause irreparable damage to the financial organization that may take years to recover. According to the Identity Force, almost 14.4 million users were subjected to identity theft fraud in 2019, and the number is increasing with each passing day.
Machine Learning algorithms can detect suspicious transactions and track customer behavior to safeguard banking entities from money laundering and terror financing activities. By learning about specific risk factors and unusual behavior in transactions, machine learning solutions deter fraudulent activities through business platforms and financial organizations by taking into account the historical data of a client.
How Are These Solutions Helpful?
ML comes with a set of advantages when it comes to preventing online identity fraud in the banking sector, which is below:
- Effective defense against identity theft
- Detect and rate the risk level of larger transactions which is hard to achieve with manual checks
- Pointing out hidden relations in data sets to track fraud
- Reduced verification measures and automatic detection with real-time processing
Comply with Financing Rules and Regulations
When operating in the banking industry, financial entities and organizations have to follow stringent compliance practices. These obligations are purpose-built to guarantee transparency in transactions and verify customer identity. They not only tend the bank or Fintech business to perform Anti Money Laundering checks but also enforce them to establish a proper means of user identity verification. Fintechs use regulatory technology (RegTech) to meet compliance requirements chartered by global watchdogs like the European Union, FinCen, and FATF. Machine learning aids financial organizations like banks to monitor transactions and identify anomalies using automated systems.
How ML Helps in Compliance?
- Banking entities can comply with regulatory requirements better since supervisory expectations using intelligent solutions are met in a good fashion.
- Manual tasks performed by humans can be replaced with accurate procedures which help in meeting compliance
- Non-compliance costs can be significantly reduced and avoided by saving business expenses.
To sum it all up, artificial intelligence and machine learning help banks, fintech, and the finance sector in optimizing loan approvals, better risk assessment to take down identity theft, and follow international rules and regulations for financial compliance.