International Journal of Automation, Artificial Intelligence and Machine Learning

Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty

Krishna Mohan Kovur — 2023-12-15

Risk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME) based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities. In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications.

Innovation Framework for Financial Excellence: Banks, FinTech and the Regulators

Ravi Kumar — 2023-12-22

Financial innovations like peer-to-peer payment and digital currency, mostly introduced by FinTech companies, are perceived as disruptions to Financial Institutions (FIs). For banks, transformation to digital banking helps initially but quickly intensifies the challenge to change with speed and scale given the diversity of such disruptions. Who are banks competing with? What will banking be competing on? Interesting questions like these, which were not even relevant a decade ago, are pushing banks to innovate about the products, services, branches, and operations, if not the very core model of banking. For example, applying pay cuts into the profit margin but emerging innovation of Blockchain raises the questions of whether banks should accept Bitcoin or re-invent the trade finance service.

Consumers do seem to benefit initially as competition encourages banks to lower the service fees and design new products in the quest of enhancing customer experience. Nevertheless, all come with a price, for instance digital frontier becomes a fertile ground for fraud and Regulators may jump in at calculated moment. This helps to level the competition or curtail intrusion into the space of customer privacy. Reluctantly, however, regulators, FinTech companies and banks now enter the impossible trinity, or what economists call the trilemma, which is the core issue our research focuses on.

This paper develops an empirical framework for banks to embrace disruptive innovation from FinTech start-ups and associated legal or regulatory changes and to create competitive advantage through strategic use of enterprise data that are originated in the bank or acquired by the bank. Lab works in multiple North American banks have been first anonymized to protect the interest of all engaged parties and then harmonized to mature the empirical models based on first-hand evidence in three directions: Customer Experience Enhancement, Digital Product Design and Fraud Management. Foundation of the framework, however, builds on research works in digital banking, social engineering, data science, decision modeling, fraud detection algorithms, digital currency, and regulations adaptive to societal transformations enabled by application of artificial intelligence and Internet of Things. Rather than locking in rigid industry trend, often heavily depend on hence limited by technology, the framework focuses on translating theory or strategy into product and actionable steps that have direct and measurable impact. Data is the key enabler. Data monetization is a good example. Results from Banking Labs work are the proof, and the value will be even more pronounced as expected results come by to validate and optimize the framework and its implementation.

Connecting Strategy, Execution and Enhance Value Creation (CSEEK) with M&A: 12 Point Success Formula

Ravi Kumar — 2023-12-26

Building a sustainable organization with organic growth, healthy ROI is complex, and the complexity will be amplified with mergers and acquisitions (M&A), particularly in financial vertical due to offerings – products and services to its customers. This paper explores a prevalent pattern in mergers and acquisitions, especially the upcoming challenges of post-integration engineering regarding business data and IT systems. We propose an adaptive strategy to mitigate these risks, cut costs, and achieve expected business growth. The most traditional models of M&A will work; however, the value realization may not be optimal, therefore, the authors propose a 12-point success formula by connecting strategy, execution and enhance value creation framework.