Abstract: The banking industry has applied automated technologies since the late 1970s, focusing on transactional, low-risk, and volumes of business-type tasks. Several banks are now investing in research and programs for intelligent data engineering to help eliminate less predictable and consequential low-impacts tasks in human and decision-making intensive processes. Functions that are needed to be augmented by data engineering’s intelligence capabilities to help improve productivity, performance, and customer service quality in these areas include investment decision-making, wealth advisory, loan issuance, model risk monitoring, model validation, regulatory compliance, market risk assessment, and credit loss estimation. However, it is unclear how the new opportunities of innovation through intelligent technologies would be implemented in existing domains without augmenting quest tools and technologies in terms of managing augmented traditions, organizational, and institutional dilemmas and challenges, and what implications the new opportunities would pose to rethink fraud-catching, risk estimating, trading, corporate colloquialism model-led change processes, treasury management, and risk-compliance management. This concern arises from the fact that financial system technologies’ prior investments and developments are already sufficiently complex and complete.
There may be an AI and data engineering-enabled revamping of data lifecycles in the banking and finance industry. The proposed adaptation may be substantially broader than the narrowly defined opportunities within well-defined and low-contest markets and domains typically approached in automating sector-specific applications. Available privacy, regulatory compliance, fairness, transparency, and explainability issues and such are already well-known in practice. The analysis suggests opening up high-consequence areas for banking and finance experts to augment their competitive intelligence to engineer robust and reliable domains or trading engines to monitor and mitigate extreme risk events for systemic risks while rethinking and reconstituting trust in sequential rules and risk choices in addition to inputs. Future work should provide in-depth analyses of the aqualism implications and apply the approach to other industries where data engineering and intelligent technologies may have a similarly profound impact.
Keywords: Financial Innovation,Artificial Intelligence (AI),Data Engineering,Risk Management,Regulatory Compliance,Banking Technology,Predictive Analytics,Machine Learning in Finance,Real-time Risk Assessment,Compliance Automation,Fintech Disruption,Big Data in Banking,AI-Driven Decision Making,RegTech Solutions,Digital Transformation in Finance.
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DOI:
10.17148/IJARCCE.2022.111249
