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Why fintech needs artificial intelligence and machine learning?

Tinuku ~ In the last two years, financial technology (fintech) companies have transformed the world's financial industry. The Fintech Association of Indonesia currently reports about 160 fintech startups in Indonesia grouped into payment services, loans, aggregators, financial planning, crowdfunding, and others. The number of online lending companies has shown the highest increase in the past year.

Tinuku Why fintech needs artificial intelligence and machine learning

The online lending company was established in response to the weakness of traditional banks that have very limited capabilities, irritating requirements and can not access by loans. In addition, traditional banking products are unable to meet the growing market needs.

Lending companies such as UangTeman overcome these challenges as opportunities by creating short-term microfinance products online to those who do not have access to traditional banks. Fintech is very flexible and offers micro-loans, short-term, no guarantee requirements, and no face-to-face meetings.

Fintech Big Data

Traditional banks solve problems based on surveys, face-to-face meetings, wet signatures, review on loan applications, and profile verification. A request is approved or denied based on the data. The whole process takes 1-2 weeks to get the funds.

Meanwhile, fintech does not have access to traditional data structures, such as credit history and bank accounts, utilities, taxes and others. Fintech lending has three major challenges: customer appraisal without loan history, low access to loan history, and validity verification.

Fintech performs an analysis using alternative or non-financial data collected from various sources, such as IP, smartphone or laptop browsing history, social media, phone usage records, geographic location, charging phone calls, and more via web, Android and IOS.

Fintech will also work with third parties including mobile operators for phone usage history, e-commerce for payment history, home power companies, government agencies including immigration, tax agencies and associations and other institutions such as gambling or casino companies.

Fintech Data Analysis

Fintech performs data analysis by reading all the big data to make informed decisions. This collection of financial and non-financial variables will help to build customer profiles, statistical models of probability and non-probability that will be used to predict the level of ability to pay off debt by prospective loan applicants.

The same method is also applied for identity verification where all lending practices use the principle "if someone gives a correct description of his profile, and the lender is confident in their ability, then the loan will be approved."

The process of analyzing the data if done manually will require very many employees and time will be days. Fintech will develop software to do it all on thousands of users. This is where the role of artificial intelligence (AI). The use of AI technology allows the analysis process in just 1 second.

But the process does not stop after lending. Lenders also pay attention to behavior patterns and loan repayment. Machine learning (ML) is a tool to automatically automate information about the borrower into the system. This tool will become more intelligent and accurate as the number of incoming data, resulting in more informed decisions.

The fintech company also consistently updates the latest technological developments to allow for faster processing. Currently, in less than 15 minutes from the beginning of the application process, prospective borrowers have received funds transfer.

Data is everything for the fintech company to realize financial inclusion to the underbanked community in a fast and large scale. The company utilizes data previously only as a passive record. The fintech company assesses the creditworthiness of unique data that traditional banks do not have.

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