Financial institutions are facing numerous challenges when it comes to scaling up analytics across business areas. This has led to increased timelines for deployment, additional costs from inefficiencies, higher attrition rates, lack of business value delivery, and more abandoned projects across key business use cases, such as AI and machine learning initiatives. To address these challenges, modeling and analytics leaders within financial institutions can deploy four types of efficiency levers to accelerate value delivery on strategic model use cases and free up capacity across model life cycle activities.
The first type of efficiency lever is automation, data, and technology enablers. This focuses on the reuse and assetization of critical components to industrialize the process, moving to a single environment for development, validation, deployment, and automation. The second lever is the delivery model and operating rhythms, which designs standardized processes and protocols with increased compression and parallelization of activities across the model life cycle, along with model inventory management. The third lever is clear, detailed standards and procedures, which establish a set of overarching objectives for the model development process, with actionable and specific guidance for developers across the life cycle. The fourth lever is the capability and skill-building plans, which establish clear roles but ensure enough cross-training and translation capabilities across the team to facilitate collaboration and interaction.
The model life cycle transformation has four key phases, and each phase should be strategically managed from concept to deployment. This process begins with a road map and communication, including understanding pain points and estimating the baseline efforts. In the design phase, enablers are chosen to prioritize quick wins, and materials are designed to train impacted groups. Next, the rollout involves the implementation of enablers designed through pilots—for example, a sample of use cases end to end. Finally, in the scale-up phase, initiatives are deployed to the remaining use cases in the model inventory.
Successful model life cycle transformation requires leaders from all key stakeholder groups across the end-to-end life cycle to be actively involved. Each stakeholder should align with the vision and come to the table without biases. An 80/20 approach should be applied, acknowledging that there will be cases where the transformation will not yield efficient results. Tangible progress should be communicated to build confidence in the leadership and functional teams and focus on quick wins. A culture transformation is critical to realizing its full potential.
In conclusion, financial institutions that successfully deploy these efficiency levers and follow these guiding principles can reduce their time to market for AI and ML use cases, increase transparency and consistency, reduce the risk of errors and attrition, and improve team health. A significant reduction in times to market for AI and ML use cases can yield a 20 to 40 basis point ROA increase for leading institutions.
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