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Artificial Intelligence can be explained as under:
“Artificial Intelligence is the one of the branch of computer science that lay emphasis on the creation of intelligent machines that can work and react like a human and to create intelligent machines”. Now it has become an essential part of the technology industry. It is a highly technical and specialized area of computer science. Some of the activities computers with artificial intelligence are designed for include:
As banks or any financial institutions are established on security and trust. As bank holds money on the behalf of its customer on trust and the customer pays off the loan on trust This trust works in both the direction i.e. bank as well as the customer. So, for the relationship to work, trust is vital.
Banks grant a loan to customers some are secured as well as some are unsecured. So, in case of unsecured loans, there is always a chance or risk of non- repayment, to reduce these risk banks use various points such as public records, sources of income, financial associations, repayment capacity and creditworthiness before sanctioning loan to anyone. But now establishing and maintaining trust in a financial relationship is becoming a more complex challenge.
As per the International Monetary Fund estimates the global debt is growing faster than world’s GDP, simultaneously number of bad loans are increasing and has reached the point where it can affect the growth of the country.
Artificial intelligence has moved beyond its early applications in automation and has evolved into Machine Learning – a set of algorithms that have the capability to change in response to their own output, or computer programs that automatically improve with experience. These algorithms do not offer a single prediction but rather offer probabilistic outputs – a range of predictions with estimates of uncertainty. Deep Learning is an evolved form of Machine Learning where a computer system is made capable of taking its own decisions by exposing it to a huge amount of data.
By processing huge amount of data from an individual’s banking transactions, information from social accounts, earning and spending patterns, friends and family history and churning it through an AI system, it is possible to create a very comprehensive credit profile. In contrast, today’s lender has limited ability to access, consolidate this data and derive actionable analytics from it. With Deep Learning, machines can be taught to make credit decisions on their own after they are exposed to a number of data points including past decisions, credit policies, risk appetite, various rules, regulations, eligibility criteria and complex scenarios.
Using their own intelligence and based on an individual’s AI-driven credit profile, the intelligent machines can now make credit decisions at speed and accuracy which humans can hardly match. Similar approaches have helped a number of FinTech companies extend credit to a much wider customer base which was previously not considered creditworthy by the banks.
With Machine Learning, AI can definitely bring tremendous efficiency and cost reduction in loan processes. Also, these machines would be far better at picking up the anomalies in applications, data points, and behavioral patterns – leading to the early identification of all kinds of possible frauds. A new study by Juniper Research predicts that unsecured consumer loans that use Artificial Intelligence or Machine Learning technology will potentially become a US$17 billion business for Fin-Techs by 2021.
At this stage, while it appears that while AI has tremendous potential to solve the trust problem in banking and financial service industry, a lot of questions need to be considered before it gets mass adoption. Trust is a delicate issue and banks should be extremely careful while dealing with it. Almost every breakthrough in technology goes through this cycle and this is no reason to cast doubt on the value that AI could unlock for both the banks and customer.
Experienced Finance and Legal Professional with 12+ Years of Experience in Legal, Finance, Fintech, Blockchain, and Revenue Management.
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