.jpg)
We are seeing monumental changes in the lending industry. Thanks to AI-powered automated underwriting, companies have significantly reduced loan processing time from weeks to hours while completely automating most documentation tasks.
Digital lending platforms enable fast decision-making through data-driven predictions, from assessing creditworthiness to allocating the appropriate sum to borrowers. They allow real-time monitoring by extracting critical data points to perform risk assessments.
Their machine-learning algorithms allow close coordination between the collection and loan origination processes. They consider various nuances of the lending pipeline and allow for more inclusive procedures in gauging would-be borrowers.
This blog will explore how document AI helps lending enterprises become more strategic and profitable by streamlining decision-making, maximizing accuracy, and eliminating the need for repetitive manual intervention.
But will it completely take over all-important human relationships? Let’s find out.
Manual review of loan applications, tax returns, bank statements, and credit reports is difficult.
The costly, time-consuming, and error-prone tasks have been replaced by loan document automation technologies, such as OCR, RPA, and intelligent loan application management systems, that increase the loan processing workflow's speed, accuracy, and efficiency.
While these tools are adept at automating manual processes and following predefined rules and formats, they fall short when it comes to large volumes of unstructured documents and datasets.
Loan document automation incorporating machine learning algorithms goes further by making sense of extensive customer data, comparing borrower profiles, and deriving insights from unstructured documents.
With the help of document AI, lending enterprises can utilize machine learning to extract critical information from handwritten applications, scanned signatures, images, and emails. Document AI employs predictive analytics and sentiment analysis to identify patterns and assist decision-making.
AI-powered document processing goes beyond rule-based workflows to supercharge your underwriting efforts with advanced risk assessment and predictive analytics.
Here's how:
Document AI can offer high accuracy rates depending on the data's quality, complexity, and processing techniques. A well-trained document AI reduces the risk of costly document extraction errors that bog down the loan underwriting process. It automates and increases the efficiency of your loan processing workflow.
With increased scalability, AI-powered loan approval methods can deliver consistently, regardless of data volume.
With over 95 percent straight-through processing time, tools specializing in document AI require little to no human intervention. Reduced manual processing minimizes the risk of errors and makes validating customer data significantly faster and easier.
AI-based extraction techniques can save businesses between 30%-40% of the hours typically spent on data extraction. As a result, employees spend less time performing repetitive manual tasks for credit analysis and channel their focus on carrying out more strategic activities.
AI-powered loan approval methods use behavioral analytics to perform complex risk assessments. While rule-based automation mimics human actions to perform repetitive manual tasks, ML models detect potential fraud and misrepresentation while increasing speed and accuracy in underwriting.
Well-trained AI-powered systems incorporate sentiment analysis to identify customer needs. Lenders can enhance the customer experience and foster loyalty by offering personalized account servicing and pricing models while adopting real-time visibility.
Moreover, ML algorithms can gather unstructured data from social and digital media platforms to gauge buyer sentiments. Enterprises can leverage this to help their underwriters serve as knowledgeable gatekeepers by implementing better pricing strategies and coverage terms.
You can gain enhanced visibility across the customer buying journey with better predictions, insights, and advanced information retrieval.
With relevant information at your quick disposal, you can approach clients with more customized plans and leverage cross-selling opportunities.
Implementing a document AI solution for lending involves first understanding the problems and objectives of the loan processing system. Lenders usually face challenges concerning the speed of loan origination, intensive reviewing, regulatory requirements, risk assessment, fraud detection, and real-time visibility.
Given below are the steps to implement document AI based on your organization’s goals and priorities.
To select the best platform for your business, ask your vendor about their software’s performance metrics. For example, if your business needs a solution to minimize human intervention, consider a tool that has a high STP rate. As per industry standards, software with an STP rate of over 90% is considered good.
Selecting the right tool also entails understanding the complexity of document types, figuring out data points, and analyzing the project cost and ROI.
Identify data sources such as loan applications, credit reports, and bank statements to train and process them into machine-readable format.
It is necessary to preprocess your data before training the AI models with it. Note that unclean data can negatively affect the precision and recall of your model.
Data cleaning, standardization, and labeling techniques help you effectively prepare your data to train the algorithm. As you advance, the model can make sense of critical data points such as credit history, compliance data, and the debt-to-income ratio.
Use the correct algorithm to train your model. For instance, historical data such as transactions and credit history play a critical role in lending decisions. And thus, supervised learning models such as logistic regression, neural networks, decision trees, and random forest are ideal for credit scoring, risk assessments, and loan approvals.
After selecting the correct learning algorithm, you can feed the model with various input features (occupation, age, employment history) and their corresponding target variables (loan approval and credit risk).
It helps the model learn patterns and relationships in the data to make decisions and predictions.
Training the model also involves adjusting parameters using deep learning, random forest, and clustering techniques. Doing this optimizes various aspects of the learning process, such as the complexity of the model, the learning rate, regularization, and so on.
After training, it is essential to test the model’s performance. Evaluation metrics include accuracy, precision, recall, F1 score, mean square error, etc. Remember to align these metrics with your specific lending objectives. For example, measuring accuracy helps understand the model’s efficiency in predicting loan defaults.
Post-evaluation, the best-performing models undergo preparation for deployment to learn more from live data. The process involves integrating its functionality into your organization's existing workflows and systems.
Deployment depends on various factors, such as the available data, hardware resources, and DevOps processes in the deployment environment. You must assess performance, scalability, data traffic, security, and version control for optimal deployment.
Implementing a document AI system has several challenges, ranging from data privacy concerns to organizational resistance. But you can overcome them with the correct practices.
The data used to train a document AI for lending carries sensitive information about your customers that can fall prey to cybercrimes. And hence, it is necessary to use the utmost discretion to protect their safety and privacy.
Methods such as privacy-preserving machine learning help prevent data leakages in machine learning algorithms.
It is ideal to obtain your customers’ consent before using their data, and you must communicate how their data will be stored, shared, and processed.
Give due diligence to fostering employee awareness and training regarding data privacy best practices.
High-quality data is accurate, reliable, and representative of diverse customers. With significant decisions, such as loan approvals, risk assessments, and medical diagnoses, depending on your model's accuracy, ensuring that the data does not carry any subjective biases is critical.
To ensure data quality, focus on data validation, cleansing, and preprocessing techniques. The process also involves implementing quality checks, establishing compliance frameworks, and leveraging domain expertise to verify the accuracy and relevance of the data used for training ML models.
Lending activities are subject to several regulations, such as the Equal Credit Opportunity Act (ECOA), the Fair Credit Reporting Act (FCRA), and the Consumer Financial Protection Bureau (CFPB), to name a few. And thus, your model must comply with relevant laws and jurisdictions.
Paying attention to the model’s accuracy, fairness, and transparency is necessary to build trust and compliance in the lending process.
Finally, if you work with a third-party vendor for document AI solutions, don’t forget to ensure they adhere to mandatory privacy standards. SOC certification is a good reference point.
Constantly track precision, recall, processing time, etc., to gauge a model’s performance against desired benchmarks. ML models are liable to degradation over time due to changes in the input and output variables and can fall prey to drifts and training-serving skew.
Establishing a feedback loop with end-users, updating your dataset, monitoring performance, and implementing version control are crucial.
Document AI systems can reflect human biases if not trained and designed carefully. Carefully processing and using data during training and evaluation is critical to avoiding discrimination based on race, gender, age, or ethnicity.
For instance, a study by UC Berkeley found that algorithmic credit scoring using big data had biases similar to humans while determining home mortgage interest rates. Both online and human lenders earned 11 to 17 percent higher profits off minority borrowers.
It is important to use diverse and representative data while training ML models. Ensure that the data is also anonymous, and avoid personally identifiable information(PII). During feature engineering and selection procedures, avoid attributes that are directly correlated with protected characteristics, such as a person’s race, age, or gender.
To discover potential disparities while monitoring and assessing your model, consider fairness metrics such as equalized odds, predictive parity, demographic parity, etc.
Above all, comply with regulations that prevent the risk of biases in lending and mortgage practices, such as the Equal Credit Opportunity Act (ECOA) and Fair Housing Act (FHA)
Legacy on-premise document processing systems involve complex steps that require highly trained staff. Their complexity makes upgrades and integrations difficult, leading to organizational resistance. Companies often prefer to stay with the same vendor for a long-time to avoid the effort and downtime of replacement.
Integration with legacy systems is also complex due to differences in data formats, technology, and the need for standardization.
One of the ways to overcome these challenges is to adopt APIs and middleware components to facilitate data exchange and communication between a document AI system and your legacy systems.
Another way is to start small with the implementation before rolling it out across the organization. You can encourage adoption and ownership by demonstrating the practical benefits of the new system, communicating your plan, and letting people take charge of the workflow design.
The developments in the field of document AI will be further driven by the need for speed, accuracy, precision, fraud detection, and fairness.
AI technology has transformative potential for automating loan applications and credit checks, enabling customers to access financial services conveniently. Additionally, it can provide valuable insights by analyzing customer feedback and preferences, allowing banks to personalize their offerings and enhance customer service.
One crucial aspect is the potential for AI to address bias in lending decisions. By leveraging machine learning algorithms, AI can analyze data objectively, removing human biases that may inadvertently influence loan approvals. It can lead to fairer lending practices, ensuring equal access to credit for all individuals.
While document AI is significantly faster, more accurate, and more objective, maintaining its fairness and ethical neutrality still requires human intervention.
ML algorithms can reflect our existing biases, and it is up to us to ensure that we first overcome them while training and evaluating the models. Monitoring data quality can help AI models make more inclusive and just decisions.
A human-centered approach will be far more effective when building customer trust and rapport. While document AI is a brilliant tool for deriving critical data-driven insights, the final decision always rests on the expertise and experience of loan advisors.
.png)
.svg)
.svg)
.png)
