Introduction to Credit Decision Engine
In today’s digital era, lenders are facing tremendous pressure to provide quick and accurate decisions on loan applications. Credit decision engines (Not to be confused with credit score factoring system) have emerged as a game-changer for lenders by enabling them to make informed lending decisions in real-time. A credit decision engine is an automated system that utilizes data analytics and machine learning algorithms to assess a borrower’s creditworthiness and make a lending decision. The system evaluates a wide range of factors, including credit score, credit history, income, employment status, and debt-to-income ratio, to determine the probability of default.
- Credit Score. A credit score is a numerical representation of a person’s creditworthiness. It’s a three-digit number that ranges i.e.- 300 to 900, and it’s calculated based on an individual’s credit history. Credit scores are used by lenders, landlords, insurance companies, and other entities to determine an individual’s likelihood of paying bills on time and managing credit responsibly.
- Income and Employment Status. Income and employment status are two important factors that can impact a person’s financial stability and access to credit.
- Income refers to the amount of money earned by an individual, self-employed or household over a specified period, typically on an annual basis. It can come from a variety of sources, such as employment, investments, self-run business, rental income, or government benefits. Lenders and financial institutions may consider a person’s income when evaluating their ability to repay a loan or credit card balance.
- Employment status refers to whether a person is currently employed, unemployed, or not in the labour force. Lenders and financial institutions may consider a person’s employment status when evaluating their ability to repay a loan or credit card balance, as well as their likelihood of continuing to earn income in the future.
- Debt-To-Income Ratio. Debt-to-income (DTI) ratio is a financial metric that measures the percentage of an individual’s monthly gross income that goes toward paying debts, such as credit card bills, car loans, mortgages, and other debts. It’s calculated by dividing the total monthly debt payments by the gross monthly income.
- Probability Of Default. Probability of default (PD) is a financial metric that measures the likelihood that a borrower will fail to repay their debts or default on a loan. It’s commonly used by lenders and financial institutions to assess the creditworthiness of a borrower and determine the risk of lending them money.
Current Trends in Credit Decisioning Across Different Geographies
Increased use of Artificial Intelligence (AI): Credit decisioning is increasingly using AI and machine learning algorithms to analyse large volumes of data to make lending decisions. AI can help automate processes, improve accuracy, and reduce risk. AI is being used in credit decisioning across many countries, including the United States, Canada, the United Kingdom, Australia, India, and China.
Alternative Data Sources: Credit decisioning is now using alternative data sources such as social media, utility bills, rental payments, and mobile phone data to assess an individual’s creditworthiness. This allows lenders to reach new customer segments who may not have traditional credit histories. Alternative data sources are becoming increasingly popular in countries such as the United States, the United Kingdom, Brazil, South Africa, and India.
Open Banking: Open banking is a trend that is gaining momentum in many countries. It allows customers to share their financial data with third-party providers, including credit decisioning platforms, to access better loan terms and rates. Open banking is being implemented in countries such as the United Kingdom, Australia, Canada, Brazil, Mexico, and India.
Personalized Credit Offers: Personalized credit offers are becoming popular across different geographies. Using data analytics, credit decisioning platforms can provide tailored credit offers to individual customers based on their credit history, income, and other factors. Personalized credit offers are being offered by lenders in many countries, including the United States, Canada, the United Kingdom, Australia, and India.
Regulatory Changes: Regulatory changes are influencing credit decisioning trends in different geographies. For example, in the European Union, the General Data Protection Regulation (GDPR) has had a significant impact on how credit decisioning platforms collect, store, and use customer data. Regulatory changes are being implemented in many countries, including the European Union, the United States, Canada, Australia, and Brazil.
Benefits of Credit Decision Engine
The use of credit decision engines has several benefits for lenders. First, it improves the accuracy of lending decisions by analysing a vast amount of data that may be beyond human capability. Second, it reduces the time required to process loan applications, enabling lenders to offer faster decisions to borrowers. Finally, it reduces the risk of human error and bias in the decision-making process, leading to more objective and consistent lending decisions.
Credit Decision Engine Solution: How it Works
The credit decision engine solution comprises several components that work together to provide fast and accurate lending decisions.
- Data Collection: The first step in the credit decision engine solution is data collection. The system collects data from various sources, including credit bureaus, bank statements, tax returns, and employment records. The data collected are stored in Servers or Clouds and integrated from various credit rating tools via API’s then analysed to determine the borrower’s creditworthiness.
- Data Analytics: The second step in the credit decision engine solution is data analytics. The system uses advanced analytics techniques, such as machine learning algorithms, to analyse the data collected. The algorithms are trained on historical data to identify patterns and correlations between borrower attributes and default risk.
- Decision Making: The third step in the credit decision engine solution is decision making. The system uses the insights gained from data analytics to make informed lending decisions. The system can approve, reject, or refer loan applications for further review based on pre-defined lending policies.
- Integration: The final step in the credit decision engine solution is integration. The system can be integrated with other systems, such as loan origination and loan servicing systems, to provide a seamless lending experience to borrowers.
How Brickendon can help?
We can provide valuable expertise and support to help design, develop, and implement a credit decision engine. We can work with financial institution or lending organization to understand their specific business needs and goals related to credit decision-making. Below are some ways by which we can help provide solutions and support on credit decision engines:
- Consulting implementation for System Design: We can help design a credit decision engine that is customized to the specific needs of a financial institution. We can help evaluate existing systems and processes and recommend improvements to create a more efficient and effective decision engine.
- Consulting implementation for Data Management: A key component of a credit decision engine is the data used to make lending decisions. We can help create a data management strategy that ensures data is collected, stored, and analysed in a secure and efficient manner.
- Consulting implementation for Technology Selection: There are many different technologies available to support credit decision engines. We can help identify the best technology solutions to support the specific needs of the financial institution.
- System Implementation: Once the credit decision engine is designed, we can help with implementation, testing, and deployment of the solution. We can also provide ongoing support and maintenance to ensure the solution continues to operate effectively.
- Consulting implementation for Compliance: Compliance with regulations and industry standards is critical for financial institutions. We can help ensure that the credit decision engine is compliant with relevant regulations and standards.
Overall, we can provide valuable expertise and support to help our clients and partners design & implement effective credit decision engines that support their business goals and meet regulatory requirements.
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