Artificial intelligence (AI) is the process of using computers to mimic the decision-making and problem-solving capacity of the human mind. AI has evolved over the years and currently, it is used by many sectors to ease the workload and provide better support to customers.
AI is slowly strengthening its grip in the banking, financial service, and insurance (BFSI) sector. The 21st century is considered the digital era, and AI is playing a great part in that. AI in the financial sector is helping the digital lending process.
You are already aware of the importance of credit scores in the lending sector. A credit score helps the lenders to understand the loan-paying capability of the borrower; hence, without any credit score, just imagine the risk lenders will possess. Now, here comes AI in the financial industry to the rescue.
The traditional lending process depends solely on credit score, recommendations, tedious paperwork, and time-consuming approval processes. For this reason, more than 50% of first-time applicants face rejections. AI financial analysis is becoming increasingly efficient in the lending process and helping banks to disburse loans more effectively. The banking industry is turning to be smarter, more advanced, and versatile due to the use of AI financial analysis
AI financial analysis aims to help banks understand the customer, their financial habits, loan repaying capability and willingness, fraud detection, etc. All these activities are performed super fast by the use of alternative data and make the digital loan disbursement process smooth for the banks as well as for the customers. Customers are experiencing flawless banking after the introduction of AI in the financial industry
If you are planning to take a loan for the first time and are worried that you will not have the desirable credit score, then AI financial analysis is there to help you out. In the world of AI financial analysis and machine learning, there is an alternate credit score system that is generated for every individual.
Moreover, 80% of the population of India doesn’t have a credit score or credit history. Earlier, it was difficult for financial institutions to provide loans to these demographics. However, after the introduction of AI financial analysis, financial technology (fintech) companies are trying to target the demographics without proper credit scores and credit history. Fintech companies are taking the help of AI financial analysis and machine learning to create an alternate credit score for individuals. Machine learning and AI financial analysis work on alternative data. These data range from where you eat, places you visit, your shopping habits, to the phone you use. All the data are then fed to machine learning to create a profile for the borrower.
Data are the new GOLD! Financial companies are buying your data from the apps you use, websites you surf, check-ins you do at different retail shops, etc. All these data clubbed together will throw a pattern on your earning and expenditure habits. After analyzing the habit, a credit score is assigned to you, which tells the financial institutions whether you will be capable or willing to repay the loan.
Mr. A is planning to opt for a loan for personal use. He has not taken any loan earlier; therefore, he doesn’t have a credit history or score. Mr. A planned to apply for the loan online. So, he visited a fintech company website and started applying. The application had a set of questions that ranged from where Mr. A worked to which phone he was using. Once the questions are answered, the fintech company will collect live data and match it with the answers provided. If Mr. A mentioned that he works in a company at a particular location for 9 hours a day and his cell phone throws a different record, then the application will be rejected. AI financial analysis and machine learning can also study human behaviour and confirm whether they are telling the truth or not. This allows the fintech company to understand whether the borrower has genuine requirements for a loan or not.
AI financial analysis has helped the banking industry to venture into new demographics and ease the lending process. Here are few advantages mentioned below:
The use of alternative credit scores for the lending process has helped fintech companies to penetrate deep into the lending market. Now financial companies can venture to demographics that don't have a credit score or history. Mainly students, retirees, immigrants, and daily wage labourers find it difficult to get a loan from the traditional lending process. However, AI financial analysis allows financial companies to prepare alternative scoring systems for individuals by following the bill payment patterns, expenditure, check-ins, etc. This allows the fintech company to give loans and also minimize the risk of fraud.
AI financial analysis helps in solving issues faced by the traditional lending process and provides a faster resolution. The alternate credit score allows AI to make instantaneous credit decisions. Nowadays our life depends on mobile apps, so it is easier for fintech companies to get permission-based data from users that helps to build a better alternative credit score for individuals; this eases the credit lending process.
The traditional lending process is effective; however, it is time-consuming and has loopholes. So, when you combine the traditional lending process with an alternate credit score system, you get a fast and effective lending process. The alternate credit score is prepared from data such as monthly bill payment history, salary, payment history, and online purchase history. These are real data and reflect the habits of an individual. Hence, using this data, the employer can make an informed decision and reduce the risk of fraud.
Nowadays, there are lots of analytics startup companies that are working on building the perfect alternative credit score. They are tying up with large non-banking financial companies (NBFCs) and helping them to provide personal loans to individuals without a traditional credit score. Shriram City Union Finance, a Chennai-based NBFC, tied up with CreditMantri to provide personal loans to its customers using an alternative credit score system. CreditMantri is a digital credit score marketplace and is helping Shriram City Union Finance to launch a score builder that helps in alternative credit scores. According to the Chief Operating Officer of Shriram City Union Finance, the new score builder has helped Shriram City Union Finance to increase the portfolio growth by 7 times.
AI financial analysis has helped many loan seekers to get loans without a credit score. The alternate credit score system is more realistic and helps fintech companies to get an idea of the real-life financial situation of the borrower. The AI financial analysis has helped the financial industry to penetrate deeper and reach the unearthed segment. This has helped the financial institutions to increase their revenue by many folds.