Financial institutions face a multitude of risks linked to operations, governance, and compliance. Because of the sector’s importance to global economies and governments alike, risk management of market and regulatory pressures has a higher priority. In 2023, the industry’s take on risk management will be shaped by two key trends – changes in structural decision-making and accelerated development and customization of digital applications. Here is a closer look.
Risk departments at financial institutions are experiencing significant transformation thanks to the rise of secure modular applications in their tech stack.
Traditionally, financial institutions’ risk departments leaned toward managing their risk tech in-house. Concerns about data protection and regulatory demands deterred some from outsourcing at least some of their technology capabilities and utilizing cloud-based technologies.
As cloud computing expands across all sectors of the global economy, the benefits of deploying at least some elements of an institution’s risk tech on the cloud have become obvious. Put simply, the cloud is essential to leverage the benefits of a varied tech stack. In addition, cloud-based services accelerate innovation in the sector.
Some of the changes in the sector are driven by regulatory requirements rather than pure market forces. Where banks and financial institutions previously faced fines resulting from failed audits, they increasingly need to demonstrate ongoing compliance. Regulatory controls have moved from being a once-a-year event to becoming preventative, ongoing measures. Cloud-based solutions and automation allow financial institutions to meet those demands without disrupting operations.
Built-in flexibility
Speed and adaptability are two of the defining factors of successful 21st-century financial institutions. Remaining competitive relies on a company’s ability to launch new products and services before a competitor does. Without an equally adaptable tech stack, product launches are at risk of being delayed, and business opportunities may be lost in the process.
When in-house risk systems cannot react quickly to a changing risk landscape or calculate real-time data as needs change, the financial institution loses out. Leading cloud-based risk technology solutions are built with flexibility in mind. Rather than relying on infrequent software updates, agile cloud risk tech is updated almost continuously.
Clients benefit from instant access to new methodologies and capabilities, including increased protection from emerging threats. Managing banking and financial risks is an ongoing task that continuous cloud-based services are better suited to meet than static in-house systems.
Higher levels of security
Thanks to recent technological advances, data security concerns have been turned on their head. Previously, financial institutions shied away from cloud-based solutions because of the perceived security risk. As cloud infrastructure is not only expanding in reach but also increasing in sophistication, cloud-based risk tech now offers stronger levels of protection than most in-house risk architecture.
According to Deloitte, large parts of the sector are in the middle of a transition process. 35% of participants in recent research expected to deploy some of their credit risk tech stacks on the cloud. While that number appears low, some of the sector’s largest firms are among those choosing cloud-based services right now.
For example, DBS started its digital transformation as early as 2014 with significant investments in changing the architecture of its tech stack. Today, almost all of its applications are cloud-enabled or cloud-native. Companies that are not yet moving toward cloud-based tech stacks may be at risk of losing their competitive edge.
Catalyzed by more scalable, faster, and secure microservices, apps, and modular systems, banks are deploying structural decision-making about API-centric architectures.
The number of digital touchpoints in the banking and finance sector continues to increase. Just a few years ago, basic banking functions were considered difficult to transform into digital processes. Today, that transformation is actively happening, and it has been made possible by application programming interfaces (API).
APIs are digital bridges between banking applications and physical bank accounts. They are the technology behind mobile wallets and the drivers of large financial transactions between businesses. APIs facilitate communication and transactions between two inherently incompatible platforms, such as an online store and a customer’s bank. Users may only click ‘pay,’ but the API goes to work and connects the shopping platform with the user’s bank.
As a result, API-centric risk tech architectures can do more for financial institutions. Aside from making connections between different platforms, they can support a strategic approach to creating a solid risk tech stack. In today’s terms, this allows for application integration from different vendors, treating them as building blocks that result in a functional, adaptable tech stack.
The risk attached to this degree of openness in the system is that the company loses control of its data and processes. API-centric architectures can help with this aspect, too. Using them to build centralized control frameworks allows financial institutions to increase their levels of compliance, operational agility, and oversight.
Structural decision-making also needs to take into account legacy systems. Their integration has been historically challenging due to proprietary technologies forcing companies to commit to a single vendor. These business models are becoming outdated, making it easier for companies to avoid system lock-in.
Banks operationalize everything with DevOps, MLOps, microservices, and no-code/low-code applications.
Around the world, financial institutions want to speed up software deployment – safely. Two of the most popular approaches are DevOps and MLOps. DevOps supports error detection during the development process and makes it easy for teams in different locations to communicate. With the help of specific tools, project progress becomes predictable.
DevOps works well in risk management contexts thanks to its ability to connect software development and IT engineering in real-time. This success paved the way for MLOps, which utilizes machine learning tools and procedures to accelerate the path of a risk management model from idea to production.
As the demand for accelerated digital transformation in the financial sector has grown, low-code or no-code solutions have become increasingly popular alternatives to traditional software development. Either approach allows users with limited knowledge to access ready-made code elements and customize them to the organization’s needs. Low-code/no-code approaches use API modular architectures to add solution elements as selected by the user via a simple interface. The goal is to reduce the time it takes to develop and deploy risk tech while also freeing up the time of highly qualified software engineers.
Taking this approach carries inherent risks if there is a gap between the existing code and the needs of the organization. This risk can be mediated by ensuring a financial institution chooses well-thought-out and well-written code.
Overall, the growing move toward operationalization of risk tech is an integral part of the sector’s move toward integrating analytics into risk management processes. This transition will change vendor relationships as network-oriented products gain ground.
Model Risk Management typifies financial sector transformation
When financial institutions evaluate the potential risks associated with their decisions, they use a model to simulate economic circumstances, statistical and mathematical assumptions as well as financial theories. This process also applies to decisions about using cloud-based risk technology. When models malfunction or are used incorrectly, their predictions will also be invalid.
Model risk management (MRM) is the process used to actively prevent errant predictions and avoid adverse consequences of decisions based on the misuse or malfunction of models.
In the finance sector, models tend to be highly complex, reflecting the consequences of the decisions made based on them. . As model inventories evolve and grow, MRM teams need to make technological advancements to keep up. While automation can help teams accelerate and scale operations, traditional solutions can’t handle the demands of today.
By utilizing an API-based modular architecture in model risk management, applications can integrate seamlessly across multiple lines of defense, allowing model inventory systems to communicate directly with development and validation platforms. Further, LCNC (low-code/no-code) applications are becoming a necessity in MRM due to tightening regulations and changing market conditions. LCNC modular solutions provide a self-serve ability to make changes to address new needs and fill data gaps, rather than performing time-consuming backend adjustments or costly platform replacements that disrupt business operations.
As financial institutions are looking to change their risk tech stack from in-house to cloud-based and from vendor-dependent to modular, this targeted approach to MRM allows them to assess risk levels faster and more precisely.
Conclusion
Digital technology is changing the financial industry as we know it. Traditional banks and financial institutions need to embrace those changes if they want to avoid losing customers. Utilizing cloud-based services will become inevitable, leading to major changes in how institutions tackle risk management. One of the key trends is the rise of a modular risk architecture that integrates the strongest, most suitable solutions from various vendors.
As banks and financial institutions employ faster, more scalable applications, risk management must reflect this adaptability to comply with regulatory requirements and ensure data protection. By choosing API-centric risk architectures and changing MRM procedures, financial institutions can harness the power of digital technology while minimizing associated risks for themselves and their customers.
