In the evolving financial landscape, the integration of artificial intelligence, big data, and predictive analytics is reshaping risk management and collections. Harshita, a seasoned expert in financial analytics, has been at the forefront of this transformation, pioneering AI-driven segmentation models and optimizing collection strategies. Her book, Computer Applications in Research, distills years of expertise into a structured guide for finance professionals navigating this shift.

“Traditionally, financial decision-making relied on historical data,” Harshita explains. “Now, we’re seeing predictive and prescriptive analytics take center stage, allowing financial institutions to move from reactive to proactive strategies.”

Her work highlights how predictive analytics enables institutions to identify delinquency patterns early, while prescriptive analytics personalizes repayment plans based on behavioural insights. This approach is revolutionizing collections, moving away from one-size-fits-all strategies toward tailored interventions that improve both recovery rates and customer relationships.

With extensive experience in home lending and credit card portfolios, Harshita has led AI-driven initiatives that differentiate between temporary financial hardships and long-term default risks. “AI models can analyse payment behaviours, economic conditions, and customer interactions to anticipate financial distress before it escalates,” she notes. “This means institutions can intervene at the right time, with the right approach.”

Her book delves into AI-powered segmentation, demonstrating how real-time adaptation of outreach strategies enhances financial recovery while maintaining customer trust. By focusing on data-backed decisions, institutions can allocate resources more effectively, ensuring that high-risk accounts receive the necessary attention while low-risk borrowers receive more flexible solutions.

However, Harshita emphasizes that AI in finance must be both powerful and responsible. “Explainability and ethical AI frameworks are critical,” she asserts. “We must ensure that machine learning models remain transparent and unbiased, preventing discriminatory decision-making in risk assessment and collections.”

Her book offers a comprehensive roadmap to achieving responsible AI implementation, addressing regulatory compliance challenges, algorithmic bias, and data privacy concerns.

Beyond theory, ‘Computer Applications in Research’ is a practical guide for professionals seeking to integrate computational research into financial services. Through case studies and hands-on techniques, Harshita provides actionable insights on AI-driven decision-making, debt recovery optimization, and risk management strategies. “My goal is to bridge the gap between research and practice, helping finance professionals harness AI’s potential ethically and effectively,” she concludes.

As AI continues to redefine financial services, Harshita’s insights serve as a guiding force, ensuring that institutions and professionals stay ahead in an increasingly data-centric industry.