Oops! Something went wrong while submitting the form.
Lenders are leveraging AI to automate document processing, improve risk assessment, streamline the loan approval process, and personalize micro-lending solutions.
AI, along with machine learning (ML) and natural language processing (NLP), helps financial institutions identify borrowing patterns to reduce the risk of non-repayment. Naturally, loan officers do not have to rely on their guts and can make better data driven decisions to reduce bank fraud detection.
In this blog, we explore the profound impact of AI on the lending and loan management industries while highlighting the challenges that come along with it.
Different areas of loan management improved by AI
AI has been enhancing the capabilities of various workflows and systems across the finance industry. Specifically in lending, its use case extends to credit scoring, loan approval, fraud detection, and collection management.
AI in credit scoring
A credit score is evaluated by analyzing borrowing history, total income, work experience, and user behavior. The system gauges the user’s borrowing and generates a scorecard.
However, the traditional method of calculating the credit score has its fair share of drawbacks.
To begin with, credit score bases most of its calculation on the customer’s historical financial data.
New customers often face difficulty securing loans even when they are creditworthy.
Next, a credit score is inapplicable for new credit products that are specifically created for the younger customer base.
It makes the financial institution short-sighted and increases the risk of making errors in their credit estimation.
Credit scoring models powered by machine learning and credit-risk analysis monitor more relevant metrics like current income level, the potential to earn, and employment opportunities to measure new customers' creditworthiness.
In short, the AI lending credit scoring system increases financial inclusion by offering credit access to a previously unattended group of people.
AI in loan approval
The traditional loan approval process is exhaustive and resource-consuming. The loan officer collects documents, such as government-issued IDs, bank statements, employment proofs, salary slips, property papers, and other relevant documents.
After compiling all the documents, key information is manually verified. After verification, it could take days, if not weeks, to get final approval from the manager.
AI-powered document processing software compiles specific information from the relevant documents at scale. It also checks the authenticity of submitted documents and routes applications through the respective departments for approval. The result is quicker loan approval times and an improved customer experience.
AI in fraud detection
According to recent reports, an average American bank faces 24,000 fraud attacks every year. Unauthorized transactions and phishing scams are the leading reasons for the increased number of attacks. But when it comes to loan fraud: identity theft and misrepresentation of financial statements are the root causes.
AI lending tackles fraud detection with four solutions; fraud scores, fraud investigation, purchase profiles, and KYC.
Fraud scores
Lenders use fraud scores to measure the legitimacy of a transaction. NLP and ML analyze data such as past transactions, fraud accidents, and risk parameters set by the lender and award a fraud score.
Depending on the severity of the fraud score, the current transaction is either flagged for review or canceled.
Fraud investigations
Artificial intelligence can scan through hundreds and thousands of loan documents in real-time and give a list of flagged loan applications that can be further investigated by a loan officer. It saves digital lending platforms thousands of man-hours that would have otherwise been spent on finding fraudulent loan documents.
Purchase profiles
By understanding user behavior, the system creates multiple purchase profiles so that financial organizations can segment customers into different profiles and monitor transactions.
KYC
AI-based KYC helps verify IDs and match fingerprints before authorizing any loans.
AI in collection management
Financial institutions analyze the bank transactions of at-risk accounts to identify customers who might default on their loan repayments. AI enables lenders to take proactive measures and create personalized loan collection strategies for these customers
For example, lenders can use targeted communications, payment reminders, and customized repayment plans for each borrower. They can set up chatbots to automate customer interactions and provide 24/7 support.
All these solutions together reduce default rates, improve collection management, and improve customer satisfaction levels through loan management automation.
Digital lending platforms use advanced machine learning algorithms to analyze massive amounts of data to identify borrowing trends. Learning lending patterns helps banks enable more precise risk assessment and optimize their credit scoring system.
Implementing automated document processing leads to 99% accurate data extraction from structured, and unstructured documents while eliminating human-prone errors. Integrating with third-party systems and business workflows helps lenders streamline the loan application process.
2. Reduce processing time to 30-60 seconds
Advanced and intelligent document processing systems extract specific information from financial statements, loan applications, and other supporting documents in seconds as compared to traditional extraction methods that take days to weeks and several verification rounds.
Going a step further, machine learning underwriting can assess creditworthiness and make precise risk assessments for faster loan approval and reduced loan processing time.
Challenges of AI in lending and loan management automation
We know that AI is only as powerful as the data sets that are available to it. However, data leakage is a common concern for users when they share sensitive financial data. For organizations using AI lending, a breach of user data could make them vulnerable to litigation and reputational damage.
Let’s look at the top challenges faced by financial institutions implementing artificial intelligence in their workflows.
1. Data privacy and security
Data algorithms need to be privy to sensitive banking information to become effective at forecasting trends and doing credit risk analysis.
Whatever software you implement for automating loan management, ensure that it is compliant with GDPR, SOC-2, and CCPA, along with regional regulatory requirements.
2. Bias and fairness concerns
The software relies on historical data to learn patterns, which might perpetuate biases and unfair treatment in the lending industry.
If the historical data contains any biases towards a specific racial group or section of society, the bias will continue unchecked through the system, and algorithms do not understand the contextual factors that might affect the creditworthiness of the borrower.
Diverse data sets, transparent AI models, and rigorous algorithm testing in loan management automation address these challenges and promote fair lending practices.
Conclusion
Docsumo helps you extract data from unstructured loan documents with more than 99% accuracy. Irrespective of the document type, digital lending platforms can quickly extract key information from the borrower’s documents. In addition, NLP models understand the context of these loan applications and segregate them for faster loan processing times.
Pre-defined workflows and APIs allow Docsumo to instantly validate the extracted information and get trained on diverse data sets. SOC-2 certification and GDPR compliance ensure peace of mind with enterprise-grade security for sensitive data.
Oops! Something went wrong while submitting the form.
By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
.png){kind=small}
.jpg)
