Data-First Finance: Architecting Scalable Data Engineering Pipelines for AI-Powered Risk Intelligence in Banking

Abstract

Banks and financial institutions face a myriad of complex challenges under global economic uncertainty. They are wrestling with shifting interest rates, credit cycles, regulatory overhauls, financial hardship, and expanding market competition. At the heart of their organizations rests the operational demand to manage and price risky financial products comprehensively, efficiently, and effectively. These products include corporate loans, individual credit agreement mortgages and unsecured loans, point-of-sale financing, residual value and car insurance, as well as more sophisticated derivatives and securitizations. All of these needs risk assessment that requires an in-depth understanding of the borrower’s probability of default, recovery in the event of default, and credit stress levels. Moreover, these organizations continuously have to make decisions about new potential borrowers. Financial institutions forecast these measures using mathematical models and optimize their strategy, like loan approval, loan pricing, collateral requirements, or collection costs, using these models. AI and machine learning are increasingly seen as the future of risk intelligence in banking, yet they represent an entirely new generation of software with no commoditized platform equivalent. As organizations are forced to grapple with legacy systems and technologies, they require a new direction to address the technical debt and unlock the potential of banks in the age of data. This paper lays out such a vision with a particular focus on scalable data engineering pipelines that enable complete re-agile experimentation with a vast array of credit risk scenarios, performance evaluation indicators, and mitigation strategies. This is the data-first architecture augmented with a high-density stack of AI workloads orchestrating multi-paradigmatic data flows to enlighten and influence business decision-making on an unprecedented scale. Implementing this vision involves optimizing data sourcing, storage, modeling, cleaning, transformation, training, serving, and inference.