According to a report by International Data Corporation (IDC) the digital world is growing at a rapid pace, and it is expected to reach 44 ZB in 2020 from 4.4 ZB in 2013. The big data technology, because of its capacity to draw useful feedback from a pool of unstructured data is expected to cross a market of 40 billion dollars by the end of 2018.
Artificial intelligence and many machine learning techniques have changed the way the enterprises work on a daily basis. The efficiency that these techniques provide is unparalleled, and many industries have acknowledged the potential of these technologies. The fintech industry is also not far behind. AI has helped fintechs to automate a variety of their processes, cut costs, and shorten the loan approval processes. Machine learning is a technique which empowers the system to learn, analyze, and predict using algorithms and past data.
Advantages of machine learning and artificial intelligence for fintechs are discussed below:
Security: Data security is the priority of any fintechs as they have a customer’s financial data. The traditional security methods like anti-viruses and firewalls have not been able to stop the malicious attacks on the systems. AI with machine learning techniques helps the fintechs to fine-tune their security as these techniques can help to identify any fraudulent behavior or suspicious transactions. AI and ML techniques can also prevent any future attack by analyzing the past data.
Processing time: AI and Ml help to reduce the loan processing time by using algorithms to check the eligibility. The loan documentation is also taken care by these techniques, which make the process faster, cheaper and error free.AL and ML techniques also help to automate the repeated process with accuracy. The automated process helps to reduce the workforce, which in turn helps the fintechs to reduce the costs for their services.
Credit scores: Credit score helps in deciding the eligibility of a customer for loan and other lending processes. The AI and ML techniques use multiple channels to find a much more accurate and granular picture of a borrower’s eligibility.
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