Over the last 25 years, the wholesale lending market has seen a tremendous uptick in change, innovation, and growth within the global financial services sector. Once a straightforward business model of borrowing and lending (i.e., ‘traditional lending’), is now multifaceted. In today’s market, trillions of dollars in loans are underwritten, traded, and used for hedging purposes globally every year. As it stands today, the loan market consists of both bilateral and complex syndicated lending and trading amongst investors. Figure 1 below illustrates the top five underwriting issuers of syndicated loans across the Americas for 2020.
With an increase in lending activity comes the need for a robust technology solution to support all aspects of the lending lifecycle. Regardless of the type of loan, the need to manage billions of dollars throughout the loan lifecycle is vital to the soundness and profitability of a given financial institution. The FDIC and OCC stated that in 2020 alone, U.S. banks accounted for $5.1 trillion in large, syndicated loans - making this a significant source of revenue for banks. [1] In the age of accelerated digital transformation, it is more important than ever that financial institutions leverage financial technologies to drive scalability, accuracy, and operational efficiency to stay competitive and avoid reputation, legal, regulatory risks.
Capturing Scalability with Fusion Loan IQ
Across the global banking sector, the vendor Finastra has been widely known as the vendor of choice for their sophisticated syndicated lending solution called Fusion Loan IQ. Fusion Loan IQ offers an end-to-end lending solution that covers various lines of businesses (LOBs) and portfolio-specific lending requirements from bilateral to complex syndicated lending. Figure 2 below illustrates how Loan IQ consolidates all LOBs under one technology stack.
Loan IQ has become the incumbent and preferred lending solution for many lending institutions due to its ability to support unique credit structures, large volumes of loans, trades, and reference data. In addition, it has the capability to support third-party integration and straight-through processing (STP). Due to changing regulatory and industry-wide requirements, financial institutions need a solution that is flexible and configurable. No bank’s book of business is the same, which further increases the need for a solution that is customizable and configurable. For example, the LIBOR transition introduced complex changes to accounting accrual calculations being felt across the industry. With Loan IQ being used as the incumbent across the industry, it was paramount that all system changes be applied in a unified and consistent way. The LIBOR transition also highlighted Loan IQs’ ability to handle changing regulatory requirements in a timely manner while protecting its client’s revenue streams. This was done by having Loan IQ provide reliable exposure values, allowing users to reprice these loans to alternative rates.
Ensuring Accuracy Amid a Dynamic Regulatory Environment
Banks in the U.S. continue to tackle challenges surrounding loan processing and data management such as legacy IT applications and processes, data quality issues, and loan processing times. As a result, large banking organizations are continuing to invest in digital solutions to address common challenges found in the end-to-end loan processing lifecycle. Currently, lenders are faced with having to manually update data into their own internal systems on an individual loan basis (i.e., interest payment notices and requests for amendments). To address the data challenge, Bank of America Corp., Citigroup Inc., and JPMorgan Chase & Co. announced they are developing a new data platform for the $4 trillion syndicated loan market that would let lenders access data across their portfolio all in one place [2]. A platform of this nature will look to improve accuracy by streamlining the challenges lenders face when making updates on each individual loan, interest payment notices, and requests for amendments. In complex deal structures, the reliance on cross-sectional data integration across banks, platforms, and third-party sources is vital to ensuring accuracy amid an increasingly regulatory environment.
Driving Operational Efficiency by Investing in Digital Technologies
With increasing regulatory costs, oversight, competition, and rising operational costs, the wholesale banking industry must achieve operational efficiency through automation technology such as machine learning and AI. Machine learning will continue to add value as operations groups look to take simplistic and redundant tasks and replace them with algorithms instead of human oversight. Making data centralized could eventually lead to shorter settlement times and improve liquidity and support growth in an asset class such as structured CLOs [2]. For example, the leveraged loans market has doubled since the sub-prime mortgage crisis, causing tremendous challenges for banks, agents, broker-deals and other market participants due to the nature of unstructured loan documentation and data.
Banks and financial institutions should look to invest in cloud technologies to fragment data costs and reduce total cost of ownership. Cloud technology, once viewed as a privacy concern by senior leaders, is now a top priority amid the on-going need to mobilize and achieve operational efficiency. Finastra’s FusionFabric.cloud Platform as a Service (Paas) combines cloud and AI technology to scan, extract, and store documents. To conceptualize the future state of loan processing, figure 3 suggests a cloud infrastructure for all critical applications and data lakes to drive operational efficiency.
As of today, we continue to see smaller lending institutions and Fintech firms (i.e., tier three banks) leverage cloud technologies to optimize efficiency and reduce costs. However, larger tier one banks are still assessing the benefits and risks associated with the move to cloud. Many banks disregarded cloud, as the mandate was to protect customer data, not customer data on the cloud [4]. Furthermore, all signs point to an “open banking” approach with APIs, leading to accelerated adoption of cloud and new technologies.
Going forward, we will continue to see global banking institutions acquire sophisticated machine learning platforms that allow for more efficient and predictable loan processing to reduce costs and improve accuracy. For more information, click here to read more about National Language Processing (NLP) to boost efficiency. OnDeck Capital uses machine learning to holistically assess applicants’ creditworthiness—considering nontraditional factors like cashflow, public records, transactional reports, and social data—to predict their ability to repay a loan [3]. Similar algorithms are being constructed in the small to medium-sized lending businesses which will further improve operational efficiency. The reliance on big data is more prevalent than ever and challenges such as data quality, data cleansing, and data management must be tackled in tandem with the Chief Data Office in order to be successful.
Accelerate Your Digital Transformation for Value Creation
Gone are the days in which traditional bilateral lending is the only form of extending credit to corporates, borrowers, and market participants. Global banking institutions are complex and partake in a plethora of business that continues to yield strong earnings. Despite the natural tendency to pay the most attention to the areas yielding the most profit in the present, banks should not only look to address the challenges that exist today, but strategize the challenges for tomorrow. Advanced software platforms, cloud solutions, data management challenges, machine learning, AI, and NLP should all be top of mind as regulatory scrutiny increases, competition accelerates, and complexity and costs continue to rise.
About Monticello
Monticello Consulting Group is a management consulting firm supporting the financial services industry through deep knowledge and expertise in digital transformation, change management, and financial services advisory. Our understanding of the competitive forces reshaping business models in capital markets and digital banking are proven enablers that help our clients drive innovative change programs to be more competitive and gain market share in new and existing businesses.
Sources:
Schroeder, P. (2021, February 25). U.S. bank regulators say pandemic drove up risk in leveraged lending. Reuters. https://www.reuters.com/article/us-usa-banks-credit-idUSKBN2AP22H
Seligson, P. (2021, April 23). Wall Street Banks Say Time for Loan Market to Ditch the Fax. Bloomberg. https://www.bloomberg.com/news/articles/2021-04-23/wall-street-banks-say-time-for-loan-market-to-ditch-the-fax
Harris, M. (2021, June 30). Four Trends In Fintech And How They’re Modernizing The Consumer Experience. Forbes. https://www.forbes.com/sites/matthewharris/2021/06/30/four-trends-in-fintech-and-how-theyre-modernizing-the-consumer-experience/?sh=3050681e7eb2
Groenfeldt, T. (2021, March 7). Finastra Offer An API-Friendly Banking Platform. Forbes. https://www.forbes.com/sites/tomgroenfeldt/2019/03/07/finastra-offer-an-api-friendly-banking-platform/?sh=11e07b72b939