Milize launched a service to streamline and optimize the loan collection process utilizing artificial intelligence (AI) . We will first provide service to Thai financial institutions that have strong needs for card loans for individuals and consumer loans, such as for cars and motorcycles.
MILIZE provides new financial services, integrating its profound knowledge of financial engineering, big data, AI, machine learning, financial planning, etc., with its strong expertise in the financial sector and system development.
With a dramatic decline in the working-age population and chronic labor shortage, improving labor productivity has become an urgent issue in Japan. On the other hand, the “100-Year Life Initiative” has further increased individual needs for asset management. MILIZE aims to solve this problem by reforming its financial consulting and counter operations of financial institutions. MILIZE makes full use of AI, IT and financial engineering; supports the efficiency and advancement of counter operations that require educational costs; and aims to establish financial services in line with FD (Fiduciary Duty).
The traditional approach to card and consumer loans would be to classify first the delinquent debtor to a higher risk group or lower risk group, based on experience and intuition. Then, the dunning for the delinquent debtors determined to be higher risk is executed on a predetermined rule basis. In the traditional case, the financial institution was allowed to take dunning action such as to call multiple times or send SMS (short mail) multiple times during the same day.
However, with the revision of the Consumer Protection Act in Thailand from the end of November 2019, the dunning action to each debtor has been restricted to once a day. In the past, it was permissible to give a redundant dunning based on experience and intuition, but from now on, it is necessary to determine “for which debtor” “when” and “on which channel” to take optimal dunning action.
As explained above, the environment surrounding the consumer finance business in Thailand is undergoing a drastic change. To respond to such changes, MILIZE developed the machine learning model which predicts the expected recovery value of each delinquent debtor when taking a dunning action for each channel. By utilizing the prediction results of this machine learning model, it becomes possible for Thai financial institutions to quantitatively determine the optimal dunning action: “for which debtor,” “when,” and “on which channel”.
MILIZE will roll out this solution to financial institutions in Asian countries such as Thailand, Malaysia, Indonesia, Philippines and Vietnam.
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