Esther Alaka Advances Risk Sensitive Financial Dashboards with Machine Learning for Transparency in Finance

By Tosin Clegg

As financial systems worldwide grow increasingly complex, a new generation of intelligent dashboards is transforming how institutions monitor risk and performance. A recent study coauthored by Esther Alaka, a Senior Associate in Financial Planning and Analysis at JPMorgan Chase, proposes a framework for risk sensitive financial dashboards embedded with machine learning to improve operational transparency and decision making.

Published in the International Journal of Scientific Research and Modern Technology (2024), the paper titled Risk Sensitive Financial Dashboards with Embedded Machine Learning: A User Centric Approach to Operational Transparency explores how integrating artificial intelligence into financial dashboards can help institutions predict, detect, and manage risks in real time. Alaka’s research reflects a growing push within finance towards automation, accountability, and trust.

According to the paper, traditional dashboards provide static financial overviews that are often limited to descriptive analytics. In contrast, Alaka’s model introduces dashboards that use machine learning algorithms to perform predictive and prescriptive analytics, allowing financial teams to respond quickly to anomalies or market shifts. These smart systems use real time data feeds and explainable AI (XAI) to make financial insights both actionable and interpretable.

Speaking on the importance of this innovation, Alaka explained that modern finance demands transparency, not just technology. “Financial leaders and regulators need tools they can understand and trust,” she said. “By embedding explainable machine learning into financial dashboards, we make analytics more human centred while still maintaining technical precision.”

The study identifies three core components that define intelligent financial dashboards: performance metrics, visual analytics, and real time alerts. Together, these features transform raw data into strategic intelligence, offering a consolidated view of key performance indicators such as capital allocation, liquidity ratios, and compliance thresholds.

Unlike traditional systems that operate reactively, Alaka’s dashboard design allows for proactive risk monitoring. With built in anomaly detection and forecasting capabilities, it can simulate multiple financial scenarios, helping institutions prepare for potential disruptions. “Predictive dashboards give decision makers a head start,” Alaka noted. “They highlight risks before they escalate and offer solutions grounded in data, not assumptions.”

The paper also emphasises the critical role of user centric design. In finance, dashboards are often used by diverse professionals, ranging from analysts to executives, and must therefore be intuitive and adaptable. Alaka and her coauthors propose interactive interfaces that allow users to personalise their view, filter information, and interpret machine learning outputs with ease.

Another highlight of the research is its focus on Explainable Artificial Intelligence (XAI). As Alaka explained, “Machine learning can only gain acceptance in finance if it is interpretable. XAI ensures users can trace why a model flagged a transaction or predicted a risk, building confidence in automated systems.”

Beyond technical design, the research addresses broader challenges such as data integrity, model bias, and system scalability. Financial institutions, the paper argues, must strike a balance between automation and human oversight to prevent algorithmic opacity, what experts call the “black box” problem. Transparency, Alaka said, is the cornerstone of sustainable AI adoption in finance.

The study situates these dashboards within a wider trend of digital transformation in financial governance, where regulatory agencies demand faster, data driven reporting. By integrating risk management tools with real time analytics, the dashboards align with global standards of accountability and corporate governance.

Alaka’s background in data analytics and machine learning strongly informed the study’s practical insights. With over five years of experience in risk mitigation and financial modelling, she has helped build scenario adjustable financial models and automated systems that strengthen cost management and forecasting accuracy. Her technical expertise bridges the gap between finance and emerging technology.

The paper concludes with a call for wider implementation of intelligent dashboards across sectors such as banking, asset management, and fintech regulation. “Our goal is to make risk analysis not just faster, but fairer,” Alaka said. “When financial systems are transparent and data is accountable, everyone, from investors to regulators, benefits.”

Through her work, Esther Alaka exemplifies a new generation of finance professionals merging analytical rigour with ethical innovation. Her research sets the foundation for AI powered transparency that could redefine how institutions approach financial governance in the coming decade.