The impact of Artificial Intelligence on the Lending business

The use of artificial intelligence in credit scoring, credit allocation, and risk management has changed the lending industry. To generate a credit score, traditional credit scoring bureaus often evaluate a borrower's credit data, such as payment history, amounts due, length of credit history, new credit, and credit mix. This approach does not consider all financial data, and it can't adjust rankings depending on new or more inclusive information. Due to their lack of credit history, subprime, underbanked/underserved, and financially challenged customers have historically had low credit scores. Over the last few decades, the banking business has seen significant transformations.

Lending techniques based on artificial intelligence (AI) are gaining traction and legitimacy. Machine learning and AI systems can examine more data to provide a more accurate response to loan requests.

Ai helps servicing underbanked clients

AI is already bringing a paradigm shift in the way retail borrowers are assessed for the personal loans.

Determining the creditworthiness of a borrower without a credit score has been a big problem for financial institutions. This problem leaves many deserving borrowers out of the credit net while lenders lose a big chunk of business. AI and Machine Learning (ML) provide a solution to this problem through predictive analytics, digital footprints and other complex algorithms and data points.

Financial service providers now can rely on the digital presence of a loan applicant, by assessing online shopping habits, utility and telephone bill payment history or even social media profiles for determining creditworthiness.

Machine learning provides credit insights

Non-numerical aspects in an applicant's creditworthiness rating, such as their consumer behaviour in other industries and social media activity, are assessed using machine learning algorithms. These cutting-edge credit scoring tools provide better insight into an applicant’s willingness to pay their debts, resulting in credit being extended to deserving candidates who might otherwise have been denied a loan.

Digital footprint analysis using AI

AI analyses data on an applicant's digital behaviour, such as the profiles of people they most regularly interact with on social media, any inconsistencies in their employment information, their purchasing habits, and the major organizations to which they belong.

The supplemental information produces a more accurate measure of their creditworthiness.

Previously, manually compiling and assessing all these distinct data points would have been too time-consuming and labour-intensive for a loan underwriter. This data is supplementing, and sometimes even replacing a credit score as the ultimate factor in whether to give credit to an applicant.

CompassWay Credit Scoring with Digital Footprints examines five indicators that surpass traditional credit models in terms of forecasting loan repayment. The five digital footprint variables are simple to use and are completely free of charge. They are as follows: the sort of computer used to submit a credit application (Mac, PC), the sort of equipment that was used (phone, pc), the data and time when the application is filed, email address, if the applicants “s name is included in their email address

Artificial Intelligence can process such data and model it to output credit scores for each customer. The credit scores are real-time and consider every transaction of the customers.

Improving risk-adjustment margins with AI

To stay profitable in the subprime and non-prime lending space, lenders (especially short-term lenders) must make smarter underwriting decisions in an increasingly uncertain credit lending environment. By utilizing AI technologies to identify worthy customers and charge them an appropriate interest rate, lenders may enhance earnings and leverage assets more effectively.

CompassWay uses a compliance approach to build ratings based on a data: lender data, customer data, which includes consented structured and unstructured publicly available data.

Credit scores are highly dependable as a result of such data integration. It enables lenders to increase loan diversity, increase loan originations, and expand their business with confidence in their decision-making.

AI and risk reduction

The use of artificial intelligence (AI) in a loan origination system reduces the risk of human mistake in processing a loan application or neglecting crucial criteria in determining whether a borrower would default on a loan. AI will also play a key role in the bank's loan management system, identifying patterns of behaviour that signal a customer is on the verge of declaring bankruptcy or defaulting on their debt entirely. The capacity to mitigate those risks will prevent costly losses and preserve credit availability for deserving borrowers who will become or remain active participants in the economy.