Methods Citations. Figures, Tables, and Topics from this paper. Support vector machine Logistic regression Smartphone Algorithm Population Baseline configuration management Formal verification. Citation Type. Has PDF. Publication Type. More Filters. Using network features for credit scoring in microfinance. Machine learning techniques for credit risk evaluation: a systematic literature review. Effective credit scoring using limited mobile phone data. View 1 excerpt, cites methods. The purpose of this study investigated the effects of saving and credit cooperative credit policies on members saving mobilization in Turkana County, Kenya.
It was guided by the following research … Expand. Software Engineering practices for building MLware applications-credit risk evaluation case study. The development and … Expand. View 1 excerpt, cites background. The essence of the financial industry is symbiotic relationships, where all participants have a winning angle, depositors get interest on their funds, project owners get funding, and the middleman gets a cut.
If financial institutions fail to offer an incentive for one of the parties, the whole system would stop working, this is why financial inclusion must serve the bottom line, because that insures the presence of an inclusive ecosystem of professionals who can work to build out services for the underserved.
When it comes to the low-income segment, banks are the ones who could not find the right angle. A staggering 1. With modest funds and no credit history, they are expensive to serve in the conventional banking channels and are often thought to have low potential to create value.
When we approached the issue, we considered three main areas of potential service offerings: payments, deposits and lending. Despite the potential for payments to integrate more people in the financial system — and still be profitable for the institutions and convenient for the customers — there was high competition in that area and financial institutions have little chance to differentiate between the services they offer compared to each other. Credit scoring is crucial for this.
Credit scoring plays a vital role in economic growth by helping expand access to credit markets, lowering the price of credit and reducing delinquencies and defaults. In the US and Europe, credit scoring helps drive the economy and makes credit affordable. For consumers, credit scoring is the key to home ownership and consumer credit. It increases competition among lenders which drives down prices.
Decisions can be made faster and cheaper, more consumers can be approved, and risk is spread more fairly so vital resources — such as insurance and mortgages — are priced more fairly. For businesses, especially small and medium-sized enterprises, credit-scoring increases access to financial resources, reduces costs and helps manage risk.
For the national economy, credit scoring helps smooth consumption during cyclical periods of unemployment and reduces the swings of the business cycle.
By enabling loans and credit products to be bundled according to risk and sold as securitized derivatives, credit scoring connects consumers to secondary capital markets and increases the amount of capital that is available to be extended or invested in economic growth. In rich countries, lenders often rely on credit scoring--formulae to predict risk based on the performance of past loans with characteristics similar to current loans--to inform decisions.
Can credit scoring do the same for microfinance lenders in poor countries? This paper argues that scoring does have a place in microfinance. Although scoring is less powerful in poor countries than in rich countries, and although scoring will not replace the personal knowledge of character of loan officers or of loan groups, scoring can improve estimates of risk.
Thus, scoring complements--but does not replace--current microfinance technologies. Furthermore, the derivation of the scoring formula reveals how the characteristics of borrowers, loans, and lenders affect risk, and this knowledge is useful whether a lender uses predictions from scoring to inform daily decisions.
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