Large Amounts of Data Determining
Posted: Tue Jan 21, 2025 10:45 am
In my opinion, data is the main driver that influences external factors affecting MFIs. That is, data, the speed of its receipt, processing, storage and transmission are of decisive importance. Client scoring in microfinance organizations The situation is changing rapidly: there is more and more data. Due to growing competition in the sector, the requirements for the quality of the information collected, the number of sources, the speed of processing and transmission are also growing.
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Under such conditions, old models, approaches and tools ceased to be effective. And everything that can be digitalized in business processes turned out to be ready for the use of mechanisms and services built, among other things, on the basis of artificial intelligence. Thus, ML services began to be introduced into a wide variety of industries, including MFIs.
The first process in which microfinance companies began to use ML services was customer scoring. Following this, machine learning-based services began to penetrate into auxiliary processes: collection, legal proceedings, etc. This trend is characteristic of both the entire industry and our company in particular. At the first stage , when the company was created, we did not have machine learning as such.
There were fairly clear, well-interpreted, but simple and not particularly selective rules. At the second stage, machine learning began to be implemented in scoring as one of the most popular areas in terms of using advanced assessment technologies. These were basic algorithms: regressions and decision trees. Later, at the third stage , using ML, we began to analyze antifraud and conduct anomaly detection.
In the fourth stage, machine learning began to spread to auxiliary departments: for example, collection and judicial collection. In the fifth stage, we began experimenting with personalization, using neural networks, i.e. we began using technologies that make some compromises with interpretability, but provide higher business efficiency.
Companies have begun to strive to make offers more bahamas telegram database personalized in order to bypass competitors and at the same time be cost-effective. Медленный сайт убивает SEO! WSR ускорит сайт в 5 раз уже сегодня! Обеспечит 100% аптайм. Улучшит ПФ. Спасите SEO! Ad w.tools Where to get money for business and how to manage finances? Experts from the educational platform for entrepreneurs "Kurs" will tell you .
Under such conditions, old models, approaches and tools ceased to be effective. And everything that can be digitalized in business processes turned out to be ready for the use of mechanisms and services built, among other things, on the basis of artificial intelligence. Thus, ML services began to be introduced into a wide variety of industries, including MFIs.
The first process in which microfinance companies began to use ML services was customer scoring. Following this, machine learning-based services began to penetrate into auxiliary processes: collection, legal proceedings, etc. This trend is characteristic of both the entire industry and our company in particular. At the first stage , when the company was created, we did not have machine learning as such.
There were fairly clear, well-interpreted, but simple and not particularly selective rules. At the second stage, machine learning began to be implemented in scoring as one of the most popular areas in terms of using advanced assessment technologies. These were basic algorithms: regressions and decision trees. Later, at the third stage , using ML, we began to analyze antifraud and conduct anomaly detection.
In the fourth stage, machine learning began to spread to auxiliary departments: for example, collection and judicial collection. In the fifth stage, we began experimenting with personalization, using neural networks, i.e. we began using technologies that make some compromises with interpretability, but provide higher business efficiency.