The Role of IPA in Transforming Lending Operation with AI

Lending is pivotal to economic development, providing essential financial access. However, navigating the lending landscape in a volatile economic environment is a challenge. Lenders face a triple threat: rising interest rates, strict regulations, and high operational costs, often leading to reduced margins. While rising interest rates and strict regulations are largely external factors over which lenders have little control, high operational costs are primarily an internal issue, offering room for proactive management. In this post, we dive into the crucial role of automation for lending businesses and how AI-native Intelligent Process Automation (IPA) can lend a hand in cheering up business operations (pun intended), driving efficiency, and boosting profitability across the board.

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IPA vs RPA: Intelligent not just robotic!

IPA represents an evolution of Robotic Process Automation (RPA), facilitating the automation of complex, non-deterministic tasks that require reasoning, logic, and planning. Unlike RPA, which operates on predefined rules, IPA introduces a layer of intelligence, harnessing a network of expert agents. In IPA, rules are dynamically generated, orchestrating specialized agents to execute smaller, more deterministic tasks towards achieving broader objectives, such as underwriting loan applications. This approach adapts to the nuanced demands of automating more complex processes that can’t be done using traditional RPA approaches.

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The Role of Secure IPA Solutions

Recent advancements in AI, especially in language and vision models, have ushered in a new era of intelligent process automation. These models have the capability to read documents and images, extracting relevant data elements for specific tasks, such as estimating a consumer's income from their historical bank statements. They can also understand customer emails, chats, and calls, and based on the lender's procedures and knowledge bases, suggest or take actions to resolve inquiries. Although data security is a concern, particularly with closed and hosted solutions, it can be integrated into these technologies to ensure their safe deployment across financial institutions.

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Optimizing Operations Through Automation

A crucial strategy for lenders facing harsh economic environments is to optimize operations through automation, aiming to reduce costs without compromising service quality. The main hurdles in this optimization journey are threefold: the scarcity of reliable and cost-effective data, lack of reliable intelligent process automation solutions, and strict data security and compliance requirements.

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• Reliable and Cost-effective Data: More granular data on entities (businesses and individuals) facilitates more efficient operations. However, the absence of reliable, cost-effective sources prohibits smaller lenders from automating more of their processes. For example, in commercial lending, publicly available data, such as state filings, license registrations, sanction lists, UCC and WARN filings, and web presence, all necessary for an automated KYB process, often resides in disparate, siloed repositories with inconsistent formats and update frequencies, making automation challenging. IPA can address these issues by enabling solutions for automatically ingesting and intelligently extracting useful information from these varied sources. For instance, an IPA solution can help sift through hundreds of state filings (often in PDFs) and free-form sanction lists to validate business legitimacy. Similarly, a business's industry code classification can be automated with a combination of small language models and classical machine learning models, often surpassing human accuracy (for more details, see BusinessMatch)

• Intelligent Process Automation Solutions: The absence of tools capable of automating non-deterministic processes requiring advanced intelligence remains a significant barrier to streamlined operations. Customer support tasks, for example, which range from answering routine inquiries to understanding customer intent, have traditionally been handled by human agents and significantly contribute to the operational expenses of organizations. These processes are ripe for automation through advancements in AI and IPA technologies (for more details, see RAIA)

• Strict Data Security and Compliance: Although the financial services sector was among the first to adopt machine learning for predictive risk modeling, its adoption of the latest AI technologies has been slow. This is largely due to strict regulations governing customer data security and compliance. Lenders must be able to explain the decisions made by these AI models while also ensuring the safety of customer data and adhering to various legal standards. Such requirements have limited the use of emerging technologies, like cloud-based AI models, for automating operations. This includes mechanisms like the detection and tokenization of sensitive information, maintaining its usefulness for AI models while protecting privacy (for more details, see AIGuard)

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The advent of intelligent automation offers a promising avenue for lenders to enhance profitability and maintain their crucial role in economic growth. Despite the slow adoption of AI technologies in financial services, secure IPA solutions present an exciting path forward. Schedule a demo with us to explore how secure IPA can streamline your operations and help you achieve a competitive advantage in the financial landscape.

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