There is an increase in the number of people asking for loans from banks as a result of the increasing population and consumption trend. Moreover, the competition between banks in terms of giving consumer loans is very harsh. Banks calculate the loan amount and the interest rate by conducting various analyses based on a scoring system that they use for their customers. Credit Scoring is a system established in connection with the credit history and various risk factors of individuals. Banks divide their customers into specific segments, try to respond to their loan requests in the fastest and most profitable manner, and use algorithms, keeping up with the digitalizing age. 


When you compare the statistical regression and the algorithms developed with machine learning, the age difference in between is very clear. Credit scores calculated by the algorithms of machine learning play a crucial role in preventing time and labor loss, and increasing efficiency. When we look at the working speed of algorithms, calculating credit score with machine learning became essential for speeding up the evaluation and decision making phases so that the banks can respond to the credit demands of customers faster.


Artificial Neural Networks, Support Vector Machines and Decision Trees are among the most frequently used algorithms. While calculating the credit score with machine learning, the algorithms ran on programs to be tested and then the outputs of the models are reviewed by examining matrices. Compared to algorithms, conventional scorecard model falls behind machine learning in terms of time, consistency and precision.
Behavioral analyses and required risk analyses may also be performed with grouping using the data analytics while defining the risk groups and customer segments. Risk profiles are determined with the lowest deviation ratio by conducting predictive analyses and reporting. Logical models composed of algorithms facilitate modeling the trends and ranking the risks, and can be easily applied to new customers. 


As Consulta, we set up Credit Scoring models featuring machine learning for our customers using our experience. We think of algorithms developed with machine learning essential for companies to develop forward-looking strategies and achieve objectives. Moreover, we advocate use of machine learning in order to have a dynamic structure that can adapt to ever changing trends.  We guarantee that the results of the algorithm-based models will be much more reliable and convenient than the ones we get from statistics.

Our solution partners

 The European House - Ambrosetti
 Software AG
 Centric Software
 Bilge Adam