Emerging Opportunities for Financial Institutions on the Road to Tech Transformation.
In the last 8 years I have been lucky enough to be able to work with (and in) the financial sector developing data models for the customer life cycle. Conceptually I wrote something here and the figure below shows it.
From being able to understand what data they needed to improve their assessments, to being able to design, develop and deploy predictive machine learning algorithms.
When it comes to scaling analytics across various business areas, financial firms confront significant challenges. Consequently, there have been longer deployment delays, higher expenditures, higher attrition rates, a lack of business value delivery, and an increase in the abandonment of critical projects across key business use cases, such as AI and machine learning initiatives. However, there are new opportunities for financial institutions to reorganize their model life cycle and generate significant value.
According to a McKinsey survey of model-risk-management (MRM) executives at 27 North American banks conducted in 2022, the size of US banks' model inventories increased by roughly 31% in 2021.
The current time-to-market life cycle for modeling might be 15 to 18 months, and pain points can be recognized throughout the process.
To address these challenges, modeling and analytics experts at financial institutions may apply four types of efficiency levers to accelerate value delivery on critical model use cases and free up resources across model life cycle activities, potentially reducing time to market.
You can read the efficiency strategies recommended by McK here, but from my perspective there are opportunities for financial organizations that go beyond productivity, which I summarize below.
Opportunities in the Financial Sector
For financial firms looking to explore new forms of alternative data, data discovery is critical. Organizations may use data discovery to find new sources of information and insights that will help them make better business decisions. Financial institutions can swiftly uncover, gather, and analyze huge volumes of data from a number of sources, including social media, online traffic, and mobile devices, by utilizing new technologies such as artificial intelligence and machine learning.
Financial institutions must adopt a holistic strategy that encompasses a variety of approaches and technologies to maximize the value of data discovery. Utilizing sophisticated analytics systems that allow data exploration, visualization, and discovery, as well as combining data from different sources to reveal hidden patterns and trends, may be required. Furthermore, financial institutions may need to work with external partners to acquire access to proprietary data sources or specialized knowledge in data science and analytics.
Process automation and standardization: Automation and standardization of data collection, preparation, model construction, validation, and deployment may reduce time to market by half. Today we have a huge amount of AI tools to help us to do this. This might result in significant time and effort savings over the model's life cycle.
Focus on reuse and assetization: Financial institutions may improve their data science models and digitalization process by focusing on the reuse and assetization of critical components. Institutions may reduce the amount of time and effort required for each phase of the model life cycle by using a single environment for model development, validation, deployment, and automation.
Define standards and processes precisely: Creating defined standards and methods for the model building process may help to improve consistency and transparency, minimizing the chance of errors and attrition.
Concentrate on cultural transformation: Finally, to realize the full potential of data science models and the digitalization process, financial institutions must concentrate on cultivating an innovative and collaborative culture. Leaders of all key stakeholder groups should be actively involved and aligned with the vision. Visible progress should be communicated to promote confidence and emphasis on quick successes.
By improving the capabilities and skills of the whole workforce, financial institutions may boost their data science models and digitalization process. In addition to clearly defined roles and responsibilities, this may incorporate cross-training and translation skills to improve collaboration and participation.
At the end, financial organizations may extract significant value from their data science models and digitalization processes by focusing on five types of efficiency levers: improve and formalized the data-discovery process, automation and standardization, reuse and assetization, clear norms and procedures, and competence and skill-building strategies. Furthermore, a culture transformation is necessary to fully realize the potential of these levers. Financial institutions that capitalize on these opportunities may unlock significant value and maintain a market advantage.