In today’s world of financial services, a well-crafted decisioning system is paramount, whether you're dealing with credit risk, fraud prevention, financial compliance, or any other critical aspect.
Occasionally, financial services organizations decide whether to buy a risk decisioning system or build one using in-house resources.
This quandary demands careful consideration of various internal and external factors before deciding. Let’s explore some of them.
It’s all about the money
First of all, a good understanding of the costs involved is important. This entails a meticulous analysis of the total cost of ownership, including future maintenance and support costs. The opportunity cost of developing a new solution that already exists in the market should be considered. All too frequently, organizations underestimate the significance of investing in full-time developers for an extended time when their utilization could be better. This results in an illusion that the in-house build is free. These developers add no value to business operations while the company carries substantial operating costs.
Another frequently disregarded cost is project governance, which tends to be higher when solely reliant on in-house IT resources for project management. This project governance is followed by change management, an ongoing activity after implementation. In the vendor-implemented option, companies always can use vendor resources if in-house IT resources are unavailable.
Do we have the time?
The second concern is time-to-market. Apart from the initial build effort, additional time may be invested due to lack of experience. Meanwhile, competitors might surge ahead by implementing “off-the-shelf” or easily configurable solutions available in the market, often in much shorter timelines.
Adherence to regulatory requirements
Thanks to pressure from numerous system users and readily available development resources, certified software can swiftly adapt to comply whenever a new regulation is issued.
For example, the proposal for cybersecurity regulations issued by the European Parliament and Council in 2022 places greater responsibility on software developers for their products and outcomes. The proposal emphasizes the vulnerability risks of using open source in such software developments.
Infrastructural regulatory requirements must also be complied with to govern analytical models used in decision-making and the capabilities and tools utilized. Such requirements are already maintained in off-the-shelf solutions.
Product support and issue resolution
Software development organizations offer teams of subject matter experts who are readily available to provide support and solutions. Because these teams support multiple clients with similar requirements, their experience, and skill sets are highly developed. With in-house development, on the other hand, it would not be easy to create a comparable team in terms of speed and effectiveness. In open-source alternatives, there are public experts available. However, their reliability is a question.
Additionally, in-house software developers’ typically high turnover causes company know-how loss and slows down the change management process. It’s very common for the lending company to come to a stage where the risk technology, which is used daily, becomes a black box for both the business and technical users.
Having said that, especially for small organizations working from offices, communication would be better since in-house developers will be onsite. Hence, the developers would understand the business requirements quickly. Also, the full developers’ team will be under the control of management and monitoring the development progress would be more accurate.
Checking on reliability and scalability
By nature, in-house systems cannot undergo rigorous reliability and scalability testing. Even if the company is happy with the existing system performance, it may fall short compared to a production environment with more complex decisioning strategies, real-time transactions, or increased volume over time – all of which might be a requirement in the future. On the other hand, off-the-shelf options are constantly being tested as they get implemented in several client sites.
Measuring reliability can be challenging, but it can be assessed and benchmarked by examining the maturity of the software.
Out-of-the-box content and system upgrades
Many off-the-shelf solutions provide out-of-the-box (OOTB) content that can be quickly implemented upon purchase or configured for custom requirements. In contrast, developing all this content from scratch is time-consuming for in-house options. Although companies may argue that their requirements are unique, decisioning needs are often quite similar across industries and regions.
Off-the-shelf solutions regularly update software to deploy new or improved functionalities and security patches. In-house systems may need more timely availability of such upgrades and patches, leaving the company vulnerable to falling behind in the competitive landscape or becoming susceptible to emerging cybersecurity threats.
Integration for end-to-end decisioning
In credit risk, compliance, and fraud processes, decisioning is the final stage where outcomes are determined. Before this, data must be prepared for modelling, offering insights from applications, customers, products, and portfolios. 'End-to-end decisioning' encompasses data, modelling, and decision-making. Recent years have shifted from product-based to platform-based decision systems with agile, AI-enabled platforms.
These systems enable real-time data analysis, decision strategy optimization, and delivery via web and mobile channels. Combining these capabilities on a single platform benefits companies by fostering real-time decisions and a customer-centric approach. With this, customers experience quicker credit decisions and improved service.
The importance of using advanced analytics
Consideration should also be given to the use of advanced analytical approaches. Several off-the-shelf decisioning platforms offer deep learning, machine learning, large language modelling and AI algorithms via their embedded analytical capabilities. Using AI necessitates governance, auditing, visualization and reporting capabilities to meet increasing regulatory requirements.
The decision to build in-house should be favored if existing technology and business know-how can significantly differentiate the targeted business model and provide a competitive advantage with capabilities unavailable in the market. It's a complex choice that requires a comprehensive understanding of the associated costs, time-to-market, regulatory compliance, support, scalability, and the potential for advanced analytics.
The decisioning system you choose will profoundly impact your financial services operation, making a choice between building and buying a crucial one.
