The modern economic model in the world directly depends on the level of technological progress. Technological progress not only forms new economic relations but also allows optimizing them. In this regard, banks are increasingly showing an increased interest in automated credit risk assessment systems that would minimize the participation of experts and the influence of the human factor on decision-making.

In this Article:

  1. What is Lending Process Automation?
  2. Why Automate and Integrate Lending and Underwriting?
  3. AI in Lending and Underwriting processes
  4. Trends in Lending and Underwriting Automation
  5. Conclusion

What is Lending Process Automation?

 Lending Process Automation

A competent assessment of the customers' creditworthiness ensures a high quality of the loan portfolio and a low level of default. Still, the bank must not only be sure that its borrowers will repay the loan: it is equally important to know who should be offered certain individual conditions and organize the entire chain of work with clients quickly. Therefore, the evaluation loan origination process needs to be automated. At its core, it is quite complex: for each individual, the loan application contains a large amount of personal information, including income and credit history — more than two to three dozen parameters in total. The bank also collects information about the presence/absence of an individual in the black lists, etc., and other related data from external sources. To unambiguously interpret the prepared parameters (after all, the parameter relationships are usually non-linear), banks previously relied on the accumulated subjective experience of expert employees, and the final credit decisions were made by credit committees, whose resources, of course, are limited.

The ability to quickly and efficiently serve a client is no longer an advantage but a requirement for any bank that seeks to consolidate its position in the highly competitive financial services market. To get into the top leaders, it is necessary to introduce new services and products in a timely manner, as well as quickly respond to changing consumer needs using a wide range of FinTech software development services.

Lending and underwriting automation technology means a set of methods and tools for creating an automated management system through the implementation of software solutions. The technology is based on the search, and often the creation, of optimal ways of information exchange within the framework of accepted control schemes.

Lending Lifecycle

Why Automate and Integrate Lending and Underwriting?

Automation of lending and underwriting processes is aimed at achieving five goals:

  • Transfer of transactions to automatic mode, which increases the speed of their processing;
  • Creation of a single accounting center to track the activities of all offices and branches of the bank;
  • Formation of a flexible product line that is suitable for customers in a particular region;
  • Ensuring high speed and efficiency of decision-making on credit processing and competent portfolio risk management;
  • Preventing fraud attempts among customers and bank employees.

Automation of the bank's operational work will make it possible to bring the procedure for registering products and providing services to a single standard. Thanks to this, customer service will be simplified and accelerated, positively affecting their loyalty to the bank. Reliable and fast operation of the bank, convenient creation, and development of products and services are ensured.

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AI in Lending and Underwriting processes

Artificial intelligence is already being used by banks to provide services to customers and improve business processes. But the heyday of this technology may be yet to come. AI in banking has accelerated access to products for many customers and automated some steps in internal processes, which also affected the speed of service.

Examples of using AI in banking automation

  1. Client scoring

Automated credit decisioning on customer applications for loan products. Previously, an application for a loan from a large business was considered for two or three weeks, and this took the time and effort of many different specialists. Now, when these applications are considered by AI, it takes no more than seven minutes from the client's request to the receipt of money. Everything happens remotely, without paper documents, and the percentage of delays is reduced to almost zero.

  1. Voice assistants and chatbots

They are used when a client contacts a call center or a bank chat to reduce service time and optimize the employees' work. Thanks to  voice robots in call centers, customers receive an average consultation 40 seconds faster, and the bank saves over $2 million per month. As for the chatbot, it processes over 40% of client requests and saves the bank more than $10 million per month.

  1. Anti-fraud and financial monitoring

AI is used to counter financial fraud by analyzing the atypical behavior of individuals and legal entities.

  1. Document processing

With the help of AI, it is possible to automatically process and enter customer data when opening loans and performing banking operations where identity verification is required. Artificial intelligence recognizes more than 70 details from scans and photos of documents for each client in 2 seconds and performs about 15 automatic data checks.

In 2020-21 lending automation software trends involved lenders' focus on matching borrowers' expectations, leveraging omnichannel, and improving loan origination efficiency. Let's take a closer look at the lending business automation trends that will shape the industry in 2022.

1. End-to-end digitization

The most important trend in lending automation will be increasing digitization. Digitization will allow banks and financial institutions to streamline their operations. These operations include customer information collection, organization, and financial analysis. The switch does not happen instantly but will take place gradually.

Based on Accenture, over 50% of lending processes are still manually performed. By using technology, banks in North America can save more than 70 billion USD by 2025. Automation can assist existing employees and increase the efficiency of financial organizations. Tech companies will also delve deeper into financial services. Automation platforms offer faster and more consistent credit approval. Loan servicing takes much less time by using digital lending platforms, and organizations can make decisions faster. 

End-to-end digitalization

2. Switch to integrative microservices 

Loan control remains a paper-extensive process. Since accuracy is important in this business, a number of guides' hard work is going into verifying documents. All steps — from mortgage utility to disbursal and collection, contain guide work. The lending organizations that also depend on those legacy structures want a further push to digitize their operations.

For customers, digitization will convey convenience. They can practice for a mortgage with some clicks. Lenders may even want to boom their reaction time by adopting automation. But automation won't constantly offer the favored outcome. In a few cases, creditors won't get the outcomes they want. The rush to update legacy structures may cost them extra withinside the lengthy run. For this, the answer is to apply integrative microservices. It permits creditors to digitize their operations at their very own pace. The modern-day fashion is cloud-primarily based microservices. Companies can upload those offerings as modules one via way of means of one. This machine permits organizations to conform to regulatory adjustments too.

3. New enterprise models

Collaboration among banking and large tech has won traction recently. Banks had been partnering with fintech for diverse offerings. For instance, NIBC financial institution uses the offerings of Oaknorth for credit score analysis. It is an instance of a collaborative version. Similarly, there exists a Point of Sale or POS transaction version. In this case, traders can be given cash through the usage of swipe machines. The aggregator version offers the purchaser all of the lending alternatives available. The peer-to-peer version permits purchasers to borrow from many creditors inclined to lend simultaneously.

4. Improved credit score structures

The credit score structures of conventional banks aren't appropriate for SMEs. There is lots of overhead with traditional scoring structures. SMEs frequently no longer get admission to credit scores due to this system. Some troubles encompass huge documentation, high-hobby rates, and lengthy decision-making times. Now, Fintechs are supplying an opportunity scoring system. They use statistics-pushed fashions alongside financial institution details. 

These statistics factors encompass:

  • Personal statistics, inclusive of age, name, touch details, financial institution credit score score;
  • Businesses' statistics like coin flows, financial institution account statements, economic statements, and POS transactions;
  • Behavioral statistics, inclusive of psychometric checks and spending patterns.

Fintech additionally makes use of different statistics factors, inclusive of schooling level, degrees, and occupation. These opportunity strategies enhance the accuracy of statistics-pushed automatic fashions. It finally reduces the time and fee of servicing a loan. Thus, quit clients can get a budget with minimum effort.


Computerization has expanded the effectiveness of various businesses around the world. Banking, including lending and loan underwriting, was, in numerous ways, an advancement pioneer. Still, the matter of starting an independent venture and business advances is as yet continued similarly it was many years prior.

The scene for business loaning is currently evolving. Prodded on by the rise of more innovation empowered contenders, numerous customary lenders are getting in on the demonstration by embracing mechanization strategies in their advanced beginning cycles. The contest is a long way from the main catalyst. Loan specialists who perceive a should be more proficient, useful, and receptive to their clients, with more significant  administration levels, also hope to carry out mechanical arrangements. These loan specialists are additionally determined by the investment funds' cost and necessities to meet more tough administrative test guidelines. For other people, the capacity to assume back command over their information and to acquire more keen, more precise business experiences is the intention.

We see as barely any, loan specialists are incited to apply computerization to decrease human knowledge in the business loaning field. Rather, most see it as an empowering agent to hold the ability and draw in financiers' experience on things that matter, like gamble examination and client relationship with the board, rather than the organization.

At last, while mechanizing advance guaranteeing systems can introduce a few difficulties, doing so can improve the establishment's brand as a pioneer and market pioneer among peers.