Digitalizing Commercial Lending Properly – Where AI Really Makes a Difference

For digital lending to run smoothly, financial institutions involved in corporate lending should not automate every subprocess indiscriminately. Only those who leverage the right aspects can fully exploit all possibilities of increasing revenue and minimizing risk. This article on the use of AI and cloud-based decision management in lending shows how banks can find the right solution for their needs, providing valuable insights into the key benefits, applications, and successful case studies.

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Main Incentives for Digitalizing the Corporate Lending Process

Christopher Hansert, Product Manager for Credit Risk Solutions at ACTICO, identifies three main incentives for banks to digitalize their lending processes: benefits in terms of revenue assurance, risk reduction, and operating costs. No financial institution should take these fundamental business aspects lightly.

Digitalization of credit risk management contributes to revenue assurance by making financial institutions more attractive. “Customer expectations are rising. They are accustomed to digital services in other areas of their personal lives,” says Christopher Hansert in a expert talk on Commercial Lending Automation. “Therefore, they also expect automated document uploads, self-service functions, and real-time decisions in the lending business.” If financial institutions neglect this, they risk losing revenue as they could lose customers to FinTech’s with shorter processing times and faster approvals.

Modern technologies for more comprehensive data analyses benefit risk reduction in lending. “New machine learning capabilities favor better models with significantly higher predictive quality. They can also evaluate additional external data sources and existing bank-internal data pools much more thoroughly,” explains Christopher Hansert. “This facilitates much better decisions and thus reduces the default risk.”

Banks can lower their operating costs by significantly increasing efficiency. “The more subprocesses of lending a financial institution automates, the more cost-efficient the business becomes,” emphasizes Christopher Hansert. “Moreover, this gives the credit analysis team more freedom to devote themselves to truly value-adding services.”

Human experts remain indispensable when large and complex inquiries require judicious decisions. Creating more resources for interpersonal exchange with significant clients also plays an important role in customer retention and, thus, revenue assurance, even in the age of AI.

Which Subprocesses Offer the Most Significant Efficiency Advantages Through Automation?

Considering cost advantages, it is tempting to automate the processes in credit risk management as fully as possible. Yet to identify and transform all suitable processes takes time and is, of course, linked to initial investments before the return is evident. Financial institutions achieve measurable benefits more quickly when they prioritize the steps with the most significant leverage effect.

The value chain of lending consists of several subprocesses. Digitizing them has varying degrees of effectiveness in increasing revenue as well as reducing credit and operational risk. “Which subprocess holds the most significant digitalization potential depends on the respective financial institution,” says Christopher Hansert. He explains in detail in the expert talk which workflow steps are generally available for selection.

The good news: Automation offers advantages along the entire workflow. It begins with product development, sales, and pricing, continues through scoring and decision-making, and extends to contract management, monitoring, and reporting. This makes it very likely that every bank can identify a process whose digital transformation will measurably advance the entire lending business.

The following three case studies show how typical financial service providers have succeeded in leveraging this potential with automation measures.

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How a Commercial Bank Successfully Automated Its Risk Analysis

The digitization of the analysis of a company’s financial position, its rating, and final reporting were three processes that one of the leading banks in the UK aimed to achieve. With an end-to-end solution from Actico, they successfully implemented it.

The main requirement was to bring together existing stock models and additional analysis models still under development on a new platform. An IRBA-compliant implementation (Internal Ratings-based approach) was of great importance.
Additionally, the bank emphasized that the analysis models should be configurable and updatable by the in-house risk management team if necessary.

The bank achieved this with a platform solution developed by ACTICO. The platform is model-independent, ensuring that it can host any internally developed rating model. A code-free operational approach enables low-threshold access for bank personnel to the system. Thanks to open interfaces of the cloud-based technical infrastructure, linking common business applications with the platform is easy.

As a result, the commercial bank now benefits in several ways from digitalization advantages. A powerful platform facilitates complex data analysis, enabling meaningful evaluations and better decisions. This reduces the risk in lending. The seamless integration of the platform with the bank’s software landscape saves time in daily business operations and thus costs. The low-threshold configuration of the (no code/low code) platform allows the hosted rating models to be quickly and flexibly adjusted, so that the financial institution can also position itself precisely for all market needs in the future.

How a Leasing Company Makes AI-Supported Decisions on Car Financing

By digitalizing as many as eight subprocesses, a Dutch car financing and leasing firm has elevated its lending to an entirely new level of efficiency.

These subprocesses include, for instance, the consolidation of data from different internal and external data sources, credit checks, reporting, and the decisioning itself. The applications from Small and Medium Enterprises (SME) provide the opportunity to be fully automated without deductions in decision quality.

The main requirement was to develop a seamless process that interacts company-wide with many different systems. Internal and external data sources had to be integrated, including information from credit bureaus and company registers. “The reliable interplay of these data points is crucial to realize an efficient workflow from the receipt of the application to the final decision, including the management of the car contract,” emphasizes Christopher Hansert.

ACTICO successfully met this requirement with a Software-as-a-Service approach (SaaS). A cloud-based system is particularly well-suited to retrieve data points from various online sources thanks to customizable web interfaces. Integrated into the system is ACTICO’s platform for Business Rules Management. This makes it easy for the credit and leasing company to flexibly create, adjust, and apply business rule models and execute them fully automatic.

As a result, the Dutch car financing company is now able to analyze very large data sets precisely in a short time during customer inquiries and make reliable decisions about lending on this basis – consistently automated, without the need for manual intervention. By successfully digitalizing those substantial parts of the workflow, the financing company benefits from significant cost advantages and a considerable reduction in risk in lending.

Properly Automating Financial Data Processing of Clients

Financial statements form a central basis for lending decisions. “Therefore, transferring these financial data from the original documents to one’s own analysis applications and processing them understandably is an important subprocess with a significant leverage effect for the entire workflow,” says Christopher Hansert. Automating this so-called financial spreading was the goal of a large Canadian commercial bank.

The main requirement was to create a much more efficient and accessible approach to processed financial data for the credit analysis team. Annual statements and other documents should be transferable much more easily and with fewer errors into the correct fields of the internally used templates than before – regardless of whether they come from a manual file upload or a web source. It was also necessary to consider that the bank serves corporate clients of different legal forms and with different business languages. Therefore, appropriately adapted templates had to be provided. Nevertheless, all documents had to be reliably stored and retrievable in a central database.

ACTICO, together with Evalueserve, developed an easily accessible web interface, allowing the credit analysis team to calculate financial ratios from captured annual statements and create extended analyses. AI-trained text recognition algorithms enable reliable extraction of data from documents in various formats and languages. The software foundation is optimized to structure the procured data according to the desired target template. This allows a very flexible processing of client data and facilitates a thorough evaluation of each credit application.

The Canadian commercial bank has successfully automated an important subprocess in this way. The increased speed of the process shortens the processing time and thus lowers operating costs. Reliable data extraction minimizes the number of inevitable human input and transfer errors. As a result, the credit analysis team receives precise figures, which favors better decisions and lowers the risk of lending. And all because the bank has transformed financial spreading into automated spreading (More on this aspect in this article).

Conclusion for AI in lending

In modern lending, the trend is towards automated processes. Because a fast-processing speed with simultaneously high predictive quality is becoming increasingly important. Only in this way can target groups with growing expectations of digital services be reached – without compromising risk minimization. Efficient workflows also benefit the cost side because highly qualified credit analysis teams can focus on value-adding services instead of routine tasks.

To make this work, the right approach is crucial. At the beginning of a digitalization project, banks should prioritize those subprocesses of lending that promise the highest efficiency gains. Three case studies of financial service providers show how individual the selection of these processes can be. Automated procurement and processing of financial documents can be as valuable as analyzing creditworthiness or even the actual decision-making.

The financial service providers mentioned have quickly achieved noticeable results with their digitalization measures because they opted for a specialized solution from ACTICO. Unlike in-house developments, the ACTICO Credit Risk Platform allows for fast, easy implementation – without having to forego customization to individual requirements.

ACTICO’s web-based solution offers interfaces to data sources and applications important for the financial industry. Thus, it integrates easily into the existing IT landscape. With pre-included templates for rating models, credit applications, and decision-making rules, the platform significantly accelerates the digitalization of banking processes. And because of the underlying cloud infrastructure, it is easily scalable and expandable with needed functions – such as the Automated Spreading from Evalueserve. This allows financial institutions to flexibly automate their lending processes, efficiently streamline risk assessment, and introduce attractive, digital credit products into the market with little lead time.

Interested in delving deeper into the topic of commercial lending?
Take a look at our expert talk on financial spreading:

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How to effectively automate financial spreading, risk rating and credit decisioning by leveraging AI and integrated SaaS solutions.

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