Deepu Komati

Deepu Komati has built a focused and fast-rising professional career in machine learning engineering and financial technology, applying predictive analytics and real-time decision systems to complex operational challenges across financial services, cloud security, and digital commerce. Over approximately five years of experience, his work has consistently centered on building machine learning models, engineering scalable data pipelines, and integrating predictive intelligence into enterprise platforms in ways that improve efficiency, reduce risk, and enhance user outcomes. Through roles at PenFed Credit Union, Capital One, Amazon, and Flipkart, he has developed a record of practical, measurable contributions that demonstrate both technical depth and meaningful applied impact.

At PenFed Credit Union, Deepu Komati made significant contributions through the design and implementation of PenCollect, a machine learning-driven collections recovery system intended to improve repayment recovery across multiple loan products, including auto loans, personal loans, education loans, and credit cards. As Lead Engineer and Data Scientist, he built predictive models to estimate repayment likelihood, developed clustering frameworks to segment customers by risk, and integrated these insights into Salesforce Financial Services Cloud to guide collections prioritization and workflow automation. The innovation of this work lay in embedding predictive analytics and behavioral segmentation directly into operational collections processes, transforming collections from a reactive process into a more intelligent and targeted system. The platform delivered measurable results, including a 12 percent recovery rate within its first year, a 30 percent reduction in operational costs through automation, and a 25 percent increase in customer response rates through more personalized outreach strategies.

He further advanced PenFed’s operational intelligence through the Payments Fraudulent Enhancements initiative, where he focused on improving payment security and transaction efficiency through real-time fraud detection and predictive analytics. In this work, he developed machine learning models to identify fraudulent transactions, used time-series analysis to forecast transaction trends, and integrated predictive systems into Salesforce workflows using APIs and Apex for automated decision-making. This project stood out because it embedded real-time fraud detection and predictive payment forecasting directly into the operational payment infrastructure, allowing the organization to both respond automatically to suspicious activity and optimize payment processing resources. His contributions led to a 40 percent reduction in payment fraud, a 15 percent increase in transaction success rates, and improved customer engagement through behavior-driven payment experiences.

At Capital One, Deepu Komati contributed to the Card Guard Fraud Detection System, a large-scale real-time fraud platform designed to detect suspicious credit card transactions with minimal latency. As a Data Engineer, he performed feature engineering on transactional behavior data, trained machine learning models including Random Forest and Gradient Boosting algorithms, and developed AWS-based scoring pipelines capable of processing millions of transactions each day. The technical significance of this system lay in its ability to analyze complex variables such as spending behavior, geolocation, device signals, and transaction timing to detect anomalies in near real time. It also incorporated continuous feedback loops for retraining and adaptation to evolving fraud patterns. Through his work on model optimization and scalable prediction infrastructure, the system achieved fraud detection accuracy above 98 percent, reduced false positives by 25 percent, and enabled near-instant identification of suspicious payment activity at very high volume.

His work at Amazon AWS Security further broadened his technical scope into cybersecurity and large-scale threat mitigation. As a Software Development Engineer on the AWS Security team, he developed automated systems for identifying and mitigating threats across AWS services such as EC2, RDS, and S3. His responsibilities included building large-scale analytics pipelines, integrating machine learning models for anomaly detection, and implementing serverless architectures using Lambda and Kinesis. The innovation in this project came from combining big data analytics, predictive security modeling, and automated mitigation mechanisms into a unified real-time threat detection framework. By helping build scalable detection pipelines and automated response systems, he contributed to reducing threat response time by 50 percent while strengthening the security posture of infrastructure serving millions of users.

Earlier in his career at Flipkart, Deepu Komati contributed to the development of a machine learning-based recommendation system that delivered personalized product suggestions using search behavior, purchase history, and cart activity. His responsibilities included designing collaborative filtering, content-based filtering, and hybrid recommendation models, along with performing feature engineering and integrating the resulting system into Flipkart’s large-scale e-commerce infrastructure. This work demonstrated innovation through the combination of multiple recommendation techniques with high-volume behavioral data to generate real-time personalized suggestions. His contributions helped drive a 20 percent increase in click-through rates on recommended products, a 12 percent improvement in conversion rates, and a 15 percent increase in sales attributable to personalized recommendations.

He also contributed to post-recommendation system performance analysis at Flipkart, evaluating the effectiveness and business impact of the recommendation engine after deployment. In this role, he conducted pre- and post-implementation analysis, assessed user engagement metrics, interpreted A/B testing results, and built dashboards for business stakeholders. This work showed his ability not only to build machine learning systems but also to rigorously evaluate their business performance through statistical analysis and behavioral insights. The resulting analysis validated measurable gains in sales, click-through rates, and conversion, while also informing continued optimization of the recommendation engine.

Taken together, Deepu Komati’s professional record reflects a strong applied orientation toward machine learning, predictive analytics, and scalable data engineering. Across collections recovery, fraud detection, cloud security, and personalized commerce, he has repeatedly contributed to systems that make enterprise operations faster, safer, and more intelligent. His work demonstrates a pattern of translating analytical models into production systems with measurable business and operational impact, marking him as a credible contributor to the advancement of applied computer science in industry.