Risk Assessment in the Digital Age: How AI is Transforming Loan Analysis

The future of lending and how AI is changing the game

Every item has a price, but can you afford it? Taking out loans are great for life’s big expenses, like buying a house, car, or paying for school. Paying the loan back can be tricky. Total household debt rose by $351 billion, or 2.2 percent, to reach $16.51 trillion in the third quarter of 2022, according to the latestQuarterly Report on Household Debt and Credit. The current default rate predicted by Fitch Ratings is 2-3% for 2023, with a 1% increase in 2024. As default rates are on the rise, financial institutions may be hesitant to hand out more loans, with the help of AI automated lending and collections, the banking industry can ensure less loan default with risk assessments and more. This is done through educated judgements on how likely a person will default on their loan, providing recommendations to get a person back on track with loan payments, and even setting up automated reminders to make sure payments are made on time.  

Automated Lending and Collections Powered by AI 

Before handing out loans, financial institutions need to move forward with a risk assessment of a person potentially defaulting on a loan and determine if the risk assessment is within acceptable margins. This is typically done with the requestor downloading a special app onto their phone through automated lending. The app determines a person’s creditworthiness by looking at their search history, how they interact on social media and other variables pre-determined by the issuing loan company. All the data the automated lending app collects is combined into a credit scoring portal for the company to look over and determine if the loan will be granted.  

Aside from determining if a loan should be granted, AI is also capable of keeping track of payments and sending reminders when payments are due. If a person’s financial situation changes or they miss a loan payment, AI can create a new payment plan ensuring the loan will still be paid. It can also quickly detect if a person is at risk of missing a payment before it happens, and course correct the payment plan ahead of time. 

It takes time to analyze each applicant’s loan request, and it’s easy to overlook or miss obtaining a key piece of data that could change the approval outcome. If you use AI for predictive analytics and risk assessment, you can reverse your fortunes, even boosting profits up to 38%. 

How Does AI Calculate the Creditworthiness Score? 

One common approach is to use machine learning algorithms that are trained on historical data to identify patterns and predict the likelihood of a borrower defaulting on a loan. This can include analyzing financial data such as income, employment history, and credit score, as well as non-financial data such as social media activity and online behavior. 

Another approach is to use natural language processing (NLP) to analyze written information such as the borrower’s income statements, tax returns, or other financial documents. Natural language processing NLP can help to extract relevant information from these documents and use it to make credit risk assessments. 

Additionally, some AI-based systems also use rule-based systems or decision tree models which can examine a set of predefined criteria to grade the risk of a borrower.  

Why Human-in-the-Loop Matters 

Humans aren’t just what a piece of paper says about them. There’s more than meets the eye and sometimes it’s what can’t be put on paper that ultimately influences decisions. Machine learning models function off the hard data they are trained with. For automated lending the model is trained on past loan applications. 

However, the algorithm may not be able to take into account all of the relevant factors a human lender would consider, such as the borrower’s personal circumstances or the context in which the loan is being taken out. By incorporating human input into the process, lenders can make sure the machine learning system is taking these factors into account, which can help to reduce the risk of unfair or biased lending decisions. 

Similarly, in the context of collections, a human-in-the-loop system could allow collections agents to review and approve or reject proposed actions generated by a machine learning system. This can help to ensure the actions taken by the system are fair and appropriate, and the rights of borrowers are being respected. 

Insight from Barron’s 

Despite the potential benefits, less than a third of financial advisors have implemented AI across their business according to research from Accenture. More than half of investors say their financial advisors provide advice that’s too generic, by leveraging AI the advice given can be customized to each customer. 

By educating themselves with terminology, leveraging AI to consider relevant data, and utilizing it to explore current applications, financial advisors are well on their way to a financial journey powered by AI. Check out the Barron’s article for additional insights on wealth management. 

How Appen Can Help 

Appen’s doc AI technology is a game-changer when it comes to automated lending. With this technology, the time-consuming task of manually inputting information from scanned documents is a thing of the past. Doc AI can quickly extract relevant information from documents such as pay stubs, tax returns, and bank statements, and make them easily accessible to lenders. This means that borrowers can apply for loans quickly and easily, without the need for time-consuming manual data entry. Thanks to Appen’s doc AI technology, lenders can process loan applications faster and more accurately, reducing errors and minimizing the risk of fraud. Overall, automated lending powered by doc AI is transformative for the lending industry, making the process faster, more efficient, and more convenient for all involved.

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