The global economy enters 2023 in a fragile and unstable state. Persistent inflation, growing geopolitical risks and tightening monetary policy will inevitably leave their mark on the industry, in particular on cash flows in different countries. To be ready for the new economic order, it is vital for banks, fintech startups and credit unions to rethink their traditional products, services, risk policy and workflows. Some businesses are on track, but many may fall behind, allowing the challenges below to prevent them from capitalizing on emerging opportunities.
‘Everything comes with an expiry date', and risk models are no exception. Due to the economic crisis, changing business needs and regulatory requirements, even the once the most accurate risk model needs to be adjusted, or even completely replaced by a next-generation model.
Legacy systems often lack the agility needed to adapt to new changes. This also applies to updating credit-risk models, which include such changes as the emergence of new metrics or algorithms that may not be supported by outdated software. Not to mention the introduction of new risk models, which may not be compatible with the obsolete system at all. In addition, with better segmentation of target customers and emergence of different financial services, there is a need for more models (sometimes hundreds of them). Of course, legacy systems don't scale well to handle this kind of workload.
The loan market is highly competitive and dynamic, so there is no room for lingering. At the same time, the introduction of new technologies and the transformation of working methods into a fully or partially digital environment takes time. In large organizations, this transformation can take three to five years, with catastrophic consequences such as the loss of customers to competitors who were quicker to take on the challenge.
The situation is exacerbated by the emergence of new-to-market lenders who do not have problems with legacy systems, quickly build, move forward and raise the bar for customer expectations. For traditional FIs who don't even know how to deal with their outdated software yet, it's hard to compete with the new-entrant market players who keep up with the current innovation trends from the very beginning. That is why banks need solutions that can be quickly and easily plugged into their workflows.
Changing processes that have been brought to automatism over many years of service is like breaking the whole system. It's painful and uncomfortable. We often resist new things at first. Scattered legacy systems, incomplete and inaccessible data, and confusing, largely manual workflows can be burdensome, but they are familiar to us. The introduction of new machine learning (ML) and artificial intelligence (AI) solutions requires employee training and the development of new professional habits. Not surprisingly, the thought of having to adapt to a changed decision-making process can make you feel bad.
One of the main reasons for the hesitation of enterprises to switch to a new system is the lack of resources required to integrate new data sources. It goes without saying that having access to a sufficient amount of reliable information is extremely important for any business, especially one that provides financing services. Access to necessary data and its analysis is a cornerstone for correct modeling of credit risk and best decision making.
Therefore, when implementing a new system, it is very important to migrate existing data without loss and error, as well as ensure seamless integration of new data sources. The right software can do most of the data management: make data available to different departments, ensure synchronization between different sources, unify different formats, and more.
Are you familiar with these headaches? Then it's time to take a pill to feel better.
Since the beginning of the existence of banking, there have been enough different tools to anticipate and avoid risks. Some of them have not survived the test of time. Some have come down to us. And although in view of today's realities they are not sufficiently effective, they are still used within the walls of conservative financial institutions. Nevertheless, it’s worth considering whether they have a place in your lending standards and practices in 2023.
The risk management module in the legacy LMS system was most likely very effective at the time these systems were built. Since they are built on computer algorithms, they were able to quickly process information and reports for credit risk assessment and decision making. However, there are doubts about the relevance of the entered data, which was taken as a basis for building risk models. What’s more, new information can hardly be integrated, as long-in-the-tooth systems and the cumbersome IT infrastructure on which they are built do not have the necessary flexibility.
Core banking is a set of software and hardware aimed to simplify the process of data management and reporting. The expansion of banking structures, the increase in the number of offices and clients requires the improvement of the quality and efficiency of the core banking system. The software at the heart of the system (often quite old) is supplemented with various add-ons as the range of banking services changes, technology develops and new standards are introduced by regulatory authorities.
Since the core banking system has a modular construction scheme, if a module fails, it can be fixed without stopping the entire system. This is a great advantage.
A threat to the security of core banking is posed by possible errors in the process of its customization. Also, there are no fully automatic banking systems yet, so there is a human factor in almost all transactions.
A fairly old and proven method is the manual underwriting, during which the underwriter examines the applicant's data to assess his ability to repay the loan. Since a credit decision is made by a person or a group of people, there is a possibility of human error. In addition, unlike a computer algorithm that can analyze a loan request in seconds, a manual process usually takes from a few days to a month.
One of the widely used tools in data modeling and financial processing is spreadsheets. The advantage of tools like excel, word documents and pdfs is simplicity and ease of use. In fact, this is a paper system familiar to many, but in digital format. As in the case of manual underwriting, all metrics and data are entered into the table manually, which means there is a high probability of making a mistake. And there were cases when a mistake made when copying data from one table to another cost the company billions. In addition, the capacity of the table is limited, which means they are not able to cope with a large amount of data.
Scoring and digital tools belong to the next-generation solutions. The Neofin team was fully aware of this and therefore used brand new technologies and capabilities in their modularized applications. An ecosystem of effective tools is designed to automate the processes of any type of loans. With end-to-end solutions like this, lenders will be able to make the best credit decisions, access credit bureau data, monitor entire or individual portfolio performance, attract clients and manage risks. Neofin’s loan software is a sweet spot between off-the-shelf market solutions that are limited in customization and can only provide basic risk assessment processes and costly, time-consuming turnkey developments. Sounds like a headache pill, doesn't it?
Want to have your say? Click here to register for the upcoming Neofin webinar on Wednesday 14 December 2022 at 18:00 GMT where industry experts will explore new ways to set risk models on internal data through a no-code solution that doesn't require integrations with core banking systems or data sources.
