Future of Intelligent Automation in lending
In a world of hyper-automation, there is no dearth of software tools that fulfil the loan origination and credit assessment needs of traditional and non-traditional lenders in the modern commercial lending industry. To increase the efficiency of present operations, industry is waking up to the need to use emerging technologies like artificial intelligence (AI) and machine learning (ML).
According to a UBS Evidence Lab analysis, 75 percent of banks with more than $100 billion in assets use AI in their operations, compared to only 46 percent of banks with less than $100 billion in assets. Moody's Analytics conducted additional market research of financial institutions in the UK and Northern EU regions, finding that while only 30 percent of lenders are currently using ML and AI solutions in their operations, 70 percent of those polled are looking for ways to incorporate these technologies for their portfolio monitoring and insights.
Automation can benefit every stage of the loan lifecycle, right from applicant identification and pre-screening through credit underwriting and customer on-boarding, portfolio monitoring, and covenant management.
Customers value quick decision-making. During the application process, new automation technologies can assist decrease inconsistencies and delays in acquiring crucial information and paperwork from prospective borrowers. This allows for a standardized, auditable, and repeatable process for each loan type, as well as ensuring that the relevant documentation is received from the borrower before the process begins. Loan officers and credit analysts obtain all the information they need to make a decision fast and accurate, leading to improved client communication and experience. On the other hand, AI & ML based credit risk assessment along with automated credit policies empowers the lender to automate most of their decisions. This gives the credit risk analyst and loan officer the freedom to understand risk behaviour at customer level and in turn make swift informed, and accurate decisions.
Moreover, in case of commercial lending, efficiently collecting financial information from businesses and owners, corporate papers, and required identity is critical to the whole credit decision making and monitoring process. The use of self-service cloud-based portals to automate these workflow phases and route them to the appropriate decision-makers enhances productivity, decreases time to closure, and eliminates redundant operations.
One of the most significant KPIs in the loan industry is client experience. The use of automation software may help to enhance this. Lending automation software is equally important for borrowers as it is for loan originators. Customers benefit from speedy choices, easy navigation on digital journey, user friendly interface, AI assisted right recommendation and access to finances. The process of engagement becomes much more approachable and less stressful. As a result, both customer delight and brand value continue to rise. It also gives the applicant more authority by giving them more control and insight over the borrowing process through online alerts and notifications.
By the end of the decade, two generations will control the majority of discretionary income: Millennials and Generation Z. These are the major target audiences for FinTech firms since they grew up with technology. Companies that implement automation as a digital filter, a key instrument for digital transformation in the banking and lending industries, have a better chance of acquiring new consumers. In the next 20-40 years, youthful borrowers are more likely to actively use loan services.
In order to remain competitive and profitable, financial institutions must guarantee that they can deliver essential finance in a fast and effective manner while maintaining a high level of customer service. The key to the future of lending is Automation, and Financial Institutions that adopt it early and successfully will reap the most benefits.
About the author
Suman Singh, CEO, CyborgIntell, is an analytics leader and data scientist with a strong background in predictive analytics and machine learning techniques across domains such as Finance, Retail, and Industrial. Have successfully built and developed a high-performance analytics team from scratch for global organizations. He has been in the industry for the past decade and a half.
