The major trends banks are adopting to tackle risk architecture issues

The banking sector has had to move quickly to digitalise its financial services in order to serve more clients than ever before. IT consumption has always been considerable in the banking industry, but a giant leap in demand for financial services has accelerated IT adoption significantly and has been remarkably beneficial in promoting financial inclusion.

Riding the wave of new technologies, banks are exploring major trends in adopting more efficient risk architectures. Their management clearly understands that having a well-designed risk architecture will allow them to offer a greater service capacity, provide effective, efficient, and agile risk assessment, and enhance their risk analysis. As financial institutions expand into new asset classes and introduce new business strategies, improving their risk architecture will also help them meet potential demands as well as their current needs.

The latest trends financial institutions are embracing to address risk architecture concerns generally include taking competitive advantage of true cloud technologies, building applications on microservices, and upskilling in data science.

Adopting these technologies makes digital transformation smoother and shows great results in fixing many related risk architecture issues, such as:

• High-cost computational and storage resources
• Poor scalability
• Low performance
• Slow data processing and analytics
• Outdated data infrastructure
• Too rigid and inefficient core technology
• Outdated processes

Combining advanced analytics with cloud technologies allows for new risk management techniques that were not possible before. These not only transform customer behaviour, but also improve risk management processes and increase customer satisfaction. The cheaper and faster computing power and data storage provided by these new technologies enable better risk decision support and process integration.

Cloud tech: unlocking its true potential

The cloud represents a completely new paradigm of how applications are built and integrated. An organisation that has reengineered its system’s risk architecture and properly transferred it to the cloud can eliminate many issues arising from its legacy systems that slow down business growth. From CompatibL’s experience, a complex project with tough deadlines and budget limitations often causes the project’s leadership to take the path of least resistance and simply “transplant” the existing software architecture to the cloud using the IaaS offering of their cloud provider. This option results in migrating the application by merely creating its mirror image in the cloud, with each on-premises server migrated to its virtualised cloud counterpart. At first glance, this approach requires little changes in the software itself but, in the long run, it deprives organisations of the tremendous value and potential of true cloud technologies.

The cloud offers a standards-based architecture that can dramatically improve the interoperability of software components. Cloud software uses standards-based RESTful APIs, standard container-based deployment strategies, and standards-based ways to acquire computing and other resources in the cloud. The standards-based nature of the cloud has proved to be a real game-changer in the digital transformation of any financial organisation.

Implementing standards-based architecture and cloud microservices helps to seamlessly integrate applications together, making them less monolithic and easier to adapt to an organisation’s evolving needs.

Big data analytics

A digital era of disruption and innovation is underway in the financial services sector, making data an extremely valuable resource. It is completely unsurprising that nowadays big data plays a crucial role in retail banking and algorithmic trading, helping to tackle regulatory, compliance, and security matters.

Many financial institutions employ big data technologies to support analytics designed to run more independently and effectively across many topical areas, such as operational risk, cyber risk, and real-time P&L and performance attribution.

Through the use of big data analytics and artificial intelligence, banks can provide their clients with more opportunities and benefits, such as better credit-risk decisions and more advanced limit management.

Machine learning risk models

Machine learning is introducing a new breed of risk and validation models offering non-subjective, advanced capabilities that surpass traditional models. Instead of a quant selecting and calibrating a model, the algorithm learns the model from the data. Compared with risk models that average and interpolate the data, machine learning models give more precise and accurate results, especially during stress periods, when machine learning succeeds where calibration fails.

Data sets with complex, nonlinear patterns can be identified with machine learning and used to build more accurate risk models. These models continue to improve as more data becomes available, further enhancing their predictive power over time. Machine learning models for interest rates and credit truly have the potential to change the financial landscape.

Machine learning models eliminate cognitive biases that affect the manual selection and calibration of models by building a consistent view of risk across multiple equities, credit names, currencies, or commodities.

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