How To Leverage AI in Lending

Take an inside look at AI in lending: where it’s been, where it’s going and how to leverage each development confidently and efficiently.

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The most familiar forms of artificial intelligence have their roots as far back as the 1950s. However, the use of AI in lending didn’t fully take off until the early 2000s, when Fannie Mae and Freddie Mac started using automated underwriting systems. 

Today, AI algorithms and technology are far more crucial parts of the lending industry, with both lenders and borrowers growing increasingly aware of potential benefits. For example, a Fannie Mae survey found that 65% of lenders are familiar with some form of AI system, and 73% say the biggest focus of artificial intelligence is improving operational efficiency in lending operations.

But how can AI technology help a financial institution make more efficient lending decisions, improve customer experience and even support fraud detection? Here’s what to know about AI in lending and how to leverage it.

The Evolution of AI in Lending

AI algorithms and similar solutions were used on and off in the lending industry as early as the 1980s, testing the waters of market analysis, risk management and fraud detection. When Fannie Mae and Freddie Mac implemented underwriting systems to analyze every mortgage lending loan application and automatically determine eligibility, a larger number of lenders began to catch on, and AI grew from a far-fetched possibility to a very real solution.

A significant part of this evolution can be attributed to Natural Language Processing (NLP). While previous AI technology focused mainly on gathering, interpreting and analyzing numerical data, NLP introduced the ability to interpret individual words and larger meanings. This is the basis for the popularity of today’s most familiar solutions, including conversational and generative AI.

Today, lenders combine numerical and language-based AI solutions to achieve operational efficiency in all facets of the lending process. Although the technology has made significant strides, much progress is yet to be realized — and many financial institutions are in the perfect position to reach out and grab these opportunities.

Revolutionizing Lending With AI

There’s a wide variety of potential applications for artificial intelligence, from loan processing and approval to customer experience improvements through a personalized lending platform. Fannie Mae survey respondents indicated that the most appealing opportunity was automated compliance review, followed by simplified fraud detection. Other possibilities included:

  • Appraisal automation.
  • Borrower income, employment and credit score verification.
  • Big data reconciliation and standardization.
  • Risk assessment and management.

Of course, lenders don’t need to stop there. AI technology presents several potential solutions for the lending landscape, such as:

Streamlining Customer Interactions

Lengthy or inefficient customer interactions don’t just delay the loan approval process; they also create friction that could lead customers to choose another financial institution in the interim. Fortunately, AI technology can streamline customer interactions by providing automated responses, answering FAQs and intuitively presenting supporting resources before a customer reaches out for help. This includes guiding users through the loan application process to avoid confusion or mistakes that could lead to delays.

Improving Customer Service

Customer satisfaction is key in securing loyal borrowers, and AI can help ensure that everything from the smallest inefficiency to the largest uncertainty can be improved, streamlined or strengthened. In some cases, this might mean automating customer service processes — for example, using a chatbot to address questions or provide support. In other scenarios, AI technology may help live agents provide quicker, more relevant answers during a call or chat.

Empowering Lending Staff

Lending thrives on smooth internal operations. But buried processes and endless documentation slow down employees and frustrate customers. AI emerges as a transformative tool with broad applications, offering efficiency gains. Its ability to swiftly sift through relevant documents and provide immediate answers reduces search times and enhances overall operational efficiency. AI proves invaluable by streamlining processes and ensuring quick access to precise information. AI empowers employees, reduces search times, and unlocks optimal outcomes.

Evaluating Credit Worthiness

Although evaluation and analysis are key parts of any credit decision, they don’t need to be arduous. Financial institutions can rely on an AI model trained to look for credit risk indicators, then either make an automated decision or escalate the account to a live expert. This helps borrowers by streamlining the process and presenting the opportunity for an immediate response, but it also helps lenders by filtering out low-complexity cases and leaving more time for more important or difficult tasks.

Simplifying Debt Collection

Of course, loan approval isn’t the end of the lending process; future steps, such as debt collection, can also be improved with the effective application of AI systems. For example, an AI tool can present borrowers with multiple repayment options and resources to explain the implications of each. Similar tools can work internally to identify potential credit risk or patterns of late payments, and then recommend the best course of action based on data-based risk assessment. 

Overcoming the Challenges of AI in Financial Services

Despite its increasing promise, artificial intelligence nonetheless represents noteworthy challenges. While these topics are relevant for any organization considering AI, it’s particularly important for the lending industry to take note of key hurdles due to both high competition and heavy regulations.

Here are a few things to review when implementing any AI model:

Data Quality

In some ways, an AI solution is only as good as the data that informs it. If you’re working with incomplete or incorrect information, your algorithm will have limited benefits — and it may even lead to problems down the road. The problem is that it’s often difficult to trace data issues to their original source; as such, it’s best to begin with an overhaul of your data capture, analysis and storage processes to ensure your AI solution has an effective foundation.


Biases come in many shapes and forms and can be fed into an AI system by the data it reads and the people responsible for creating or organizing that information. This isn’t limited to social biases such as prejudices, although these have a hugely significant impact; it can also occur when an AI tool isn’t able to consider a wide enough range of technical possibilities or variables, leading to bias in what the system can understand.

Ethical Considerations

Ethical considerations are a hot topic in conversations around AI. For example, the potential for bias introduces many questions about the fairness of processes such as automated lending decisions. It’s also important to consider the widespread worry that AI technology will eliminate the need for human workers, impacting job availability and undermining the foundations of customer service. Of course, ethical considerations aren’t worth taking into account exclusively for their own sake; there are also the reputational, financial and legal implications to contemplate.

Regulatory Compliance

Every element of financial service is highly regulated to protect consumers, and AI is no exception. The Consumer Financial Protection Bureau, among other regulators, has highlighted concerns about AI tools that may not comply with federal consumer financial protection laws. This includes inaccurate information provided to users, insufficient data and privacy defenses, potential consumer harm and more. These concerns mean that financial institutions need to be even more vigilant about compliance when implementing any AI model.

Insufficient Supervision

Although some conversations about AI focus on the potential for replacing human jobs, others emphasize an opposite issue: AI technology still requires too much oversight. In many ways, the need for consistent supervision (for example, to ensure accuracy or avoid non-compliance) undermines AI’s promise to streamline workflows, automate manual processes and free up time for more important work. 

Looking Toward the Future of AI in Lending

As lenders discover new utilizations for AI technology — and new associated challenges — these tools will continue to evolve in response to industry and consumer demands. Here’s what an increasingly automated future could look like for lending:

  • Conversational and generative AI: Language-based AI tools are likely to become more popular as customers use such systems in their everyday lives. Fortunately, conversational and generative AI can often learn from ongoing conversations, making these tools a great fit for both internal and external applications. From answering customer questions to generating certain sections of loan documents to finding answers swiftly in an information maze, these AI solutions are promising.
  • Fraud detection: The rise of artificial intelligence doesn’t just support more intelligent, intuitive fraud detection; unfortunately, it also represents the potential for new and more complex types of fraud. This means financial institutions must stay vigilant when designing solutions and invest plenty of time in market research.
  • Balances: As the industry experiments with AI and the tools themselves become more functional, the “correct” or “preferred” balance of artificial intelligence vs. human service will continue to shift. This will likely drive an increased focus on other differentiators and the underlying processes, data and workflows that make or break AI implementation.

Discover AI in Action With Posh

In a highly regulated, increasingly competitive industry, AI is more important than ever. Financial institutions have many options when it comes to AI tools — but if you want to leverage 24/7 solutions built with banking in mind, you need Posh.

Our Voice Assistant, Digital Assistant and Knowledge Assistant tools help with different elements of financial service, from customer care to automated transactions. We offer both internal and external solutions so you can build AI into your most important processes. We also have more lending-specific options coming soon.

Request a demo today to see Posh solutions in action. 

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