Toward Scalable Artificial Intelligence in Finance

Toward Scalable Artificial Intelligence in Finance

연구 분야: Artificial Intelligence

학회: 2021 IEEE International Conference on Services Computing (SCC)

Innovation in Artificial Intelligence (AI) continues to produce a wealth of techniques, mostly coming from the inductive form of AI also known as Machine Learning (ML). The majority of ML algorithms is industry-neutral and business process agnostic. ML innovation is propelled by publicly available research, which gets harvested into Open Source for wide distribution through software and Cloud vendors.Ongoing AI technology work creates an immense source of assets for data-driven modeling, delivered as software libraries. However, the application of these assets for data monetization in finance does not happen with nearly comparable success or speed. The latter challenge is commonly known as the "scalability problem of AI". As new techniques continue to grow vigorously, the investment from large finance institutions to cost-effectively produce applications for a variety of lines-of-business (LoBs) and business processes will increase. The availability of ML capabilities on Public Cloud is a way for enterprises to increase productivity by benefiting from the best AI assets available from providers and startups. But data is constrained in terms of location, access and use in most finance competences by either laws or internal Governance, Risk and Compliance (GRC) rules. Legal limitations include, and go beyond, Privacy Acts, impacting non-retail processes where AI techniques must be explained in layperson language to decision-makers and regulators before field deployment. The latter is not yet achieved satisfactorily. Lastly, a large percentage of AI projects fail, in part due to unsuitable ML modeling for analytics and forecasting problems in finance. The variety and complexity of human behavior present in most finance processes calls for understanding AI at a level of cognitive depth that has no precedent in other industries. It is imperative that AI be approached so that finance competence and functional specificity are embedded a-priori into ML techniques and not as... Show More