Elevating Financial Decisions and Loan Approval with Credit Decisioning Platform

Credit decision platforms have revolutionized the banking and lending industry by providing the capability to swiftly and accurately evaluate a borrower’s creditworthiness using data analytics and machine learning algorithms, thereby facilitating real-time lending decisions.

Not only does this streamline the loan approval process, but it also aids in risk management and ensures adherence to regulatory standards.

The credit decision platform employs a predefined set of rules, algorithms, and predictive models to examine diverse factors concerning a prospective borrower’s credit history, financial status, and other relevant details.

These factors may include:

1. Credit Score: It is a score that shows how well the borrower has been at borrowing money in the past.  
2. Income and Employment Information: The engine looks at the borrower’s income, employment history, and stability to see if they can pay back the loan.  
3. Debt-to-Income Ratio: This helps figure out if the borrower can handle more debt based on what they already owe, from their existing financial obligations.  
4. Credit History: The engine checks the borrower’s past borrowing and paying back habits to see if they are reliable with money.  
5. Loan Amount and Terms: It considers how much money the borrower wants to borrow and the rules for paying it back in the decision-making process.  

These factors aid the banks and lenders in assessing the probability of a borrower repaying their debt punctually and completely, allowing them to decide on the loan approval.

Why Banks and Lenders Are Shifting Their Mindset Towards Automating Their Credit Decisions

For many years, people have been making credit decisions by hand, looking at a borrower‘s finances to see if they can pay back what they borrow. But now, because customers and regulators want more, these manual methods often can’t keep up with the growing number of requests. This can mean decisions take longer, and it may cause dissatisfaction from customers.

Below are some of the reasons why banks and lenders are moving towards automation.  

1. Poor data quality: Manual credit decisioning is often inconsistent due to human error. Incomplete or incorrect data usually happens, and this leads to inaccurate decisions. This can impact the quality of data negatively. 
2. Time-Consuming Processes: As demand grows, there will be tedious reviews of large volumes of loan applications. When done manually, credit decisioning processes are often labor-intensive and time-consuming.  
3. Unreliable Risk Monitoring: Due to human bias and oversights, manual processes can give a higher risk monitoring process. If this goes undetected, it could lead to negligence. It’s harder to consistently adhere to complex and evolving regulatory frameworks, thus, the manual process can increase the risk of non-compliance.  
4. Lack of Transparency: It is difficult for banks or lenders to track progress or identify potential issues, due to lack of real-time visibility in manual credit decisioning performance. This leads to slow response times and approval delays. As a result, it can hurt the business’s operations.  

This is why many banks and lenders are using automated systems to make credit decisions by using algorithms and predictive models to quickly look at vast amounts of customer information. They can improve and quickly make more accurate decisions while meeting compliance requirements. This helps make things smoother for the banks and improves customer satisfaction.

Streamlining Credit Decisions: A Step-by-Step Process

The credit decision platform comprises several components that work together to provide fast and accurate lending decisions. Below is a general overview of how automation works for making credit decisions as well as understanding the whole big process.

Step 1: Application Submission 

Borrowers will provide information such as personal details, income, employment history, and other information when submitting a loan or credit application.  

Step 2: Data Collection 

The bank or lender will gather other relevant data to assess the creditworthiness of a borrower. They will obtain other information from credit bureaus, which contain information on their credit history, payment history, outstanding debts, and other details.  

Step 3: Data Analytics 

The third step in the credit decision platform is data analytics. Here advanced analytics techniques are used such as machine learning algorithms to analyze the data collected. 

The algorithms are trained on historical data and analyze large amounts of data to identify patterns and correlations between borrower’s attributes and creditworthiness.  

Step 4: Risk Assessment and Decision Making 

The system looks at data to help decide if the borrower gets the loan. It gathers all sorts of information about the borrowers such as their credit score, income, employment stability, debt levels, etc to assess the overall credit risk associated with them. The bank or lender will use all this data to decide whether to approve, deny, or modify the loan application.  

Step 5: 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 other loan servicing systems, to provide a seamless lending experience to borrowers. 

This not only helps streamline their credit decisioning process but also expedites the process, while focusing on how they can strategically innovate and transform their credit decisions by using various strategies.

How Fintech Companies are Transforming Their Credit Decisions Process

Fintech companies can take as little as a few hours to assess the creditworthiness of a borrower by leveraging machine learning algorithms. However, in the new age, lenders can easily gather data from multiple data sources. This helps them to get a clearer picture of financial stability and creditworthiness way faster.  

The result? Deserving borrowers get easier and faster access to loans. So, how do FinTech companies manage to do it?  

They harness various innovative technologies as below:  

Alternate Credit Scoring

Alternate credit scoring expands beyond the traditional factors considered by credit scoring agencies. It’s a more comprehensive scoring system that helps many consumers, especially those who are new to borrowing money, get a fair shot at credit. 

Fintech companies use alternate credit scoring methods to evaluate consumers’ digital footprints and decide if they’re likely to pay back loans. These methods look at a broader range of information, making credit scores more comprehensive. They consider other relevant information such as bank transactions, utility bills, rent payments, and more to get a better picture of a borrower’s financial habits. 

Social Media

Examining digital footprints on social media can provide valuable insights into a borrower’s traits, behavior, background, interests, and more. This data helps gauge their intentions and capability to pay back a loan accurately. 

Many Fintech companies utilize psychometric tests (an assessment tool designed to measure an individual’s psychological attributes), and digital checks to assess potential customers’ creditworthiness and repayment ability. 

When using social media data, Fintech firms strike a balance between acquiring extra information and respecting privacy. They concentrate on gathering pertinent, non-invasive data that adds to a comprehensive understanding of an individual’s creditworthiness. 

Mobile Application

FinTech mobile apps often have permission to access users’ transaction data, allowing them to analyze spending habits, income sources, and financial stability in real-time. This enables users to receive immediate notifications regarding credit approvals, enhancing transparency and responsiveness throughout the process. 

Advanced algorithms embedded within these mobile apps swiftly process the gathered data using alternative credit scoring techniques to assess creditworthiness efficiently. 

To bolster security and user convenience, FinTech mobile apps frequently integrate biometric authentication methods like fingerprint or facial recognition. This ensures the protection of sensitive financial data while providing users with access to their accounts swiftly.

How Trisilco Can Help Streamline Your Financial Decision and Loan Approval Process

Here at Trisilco, we work with our business partner, Actico to provide advanced credit decisioning capabilities to our client base in Malaysia. 

ACTICO is a top global provider of software designed for intelligent automation and digital decision-making. Their high-quality software and tools are utilized across various industries to improve daily decision-making processes and streamline automation from start to finish. 

By assisting businesses worldwide, ACTICO enables them to make more informed daily decisions, leading to increased growth and a stronger competitive edge. 

Their Credit Decision Platform is designed to automate credit origination and decision-making processes. Built on a robust graphical decision automation platform, it enables banks to implement, test, simulate, and optimize various decision services internally. These services include risk scoring, pricing, and credit decisioning strategies.

Here’s an overview of what their platform provides; 

• Application Processing: Has powerful APIs that seamlessly integrate with your credit and origination workflows.  
• Decision Strategies: Allows you to implement, deploy, and run credit decision strategies in real-time.  
• Machine Learning Algorithms: Connects and executes any number and type of machine learning models.  
• Data Enrichment: Seamlessly integrates internal and external data sources, such as credit bureaus.  
• Compliance Checks: Makes compliance checks for Anti Money Laundering (AML) prevention or screening of sanction lists.  
• Auditable Data Storage: Store application data for analyzing key performance indicators and driving model optimization.  
• Decision Automation: Automatically decides on credit applications in real-time.  

You can do all these things in an integrated, single credit decisioning platform. Powered by the Decision Management Framework, the credit decision platform supports to; 

• Give higher flexibility and agility  
• Easily deploy and execute new or updated decision strategies  
• Integrate with external data sources and internal systems  
• Simulate scoring models and decision strategies for optimized models and better credit decisions 

Contact us at marketing@trisilco.com if you need more info regarding the credit decisioning platform.