Solving the Unsung Problems in Banking Tech: Infrastructure, Performance, and Real-Time Risk – A Look into Saikrishna Garlapati's Work

In the banking environment, the conversation around fintech often revolves around customer-facing products, apps, digital wallets, and AI chatbots. But behind those polished interfaces lies infrastructure, which upholds all together. Saikrishna Garlapati has spent his career focusing on the problems that the infrastructure underneath these activities might face, helping change how banking systems run under the hood.

With a background that blends technical rigour with industry applicability, Saikrishna has authored research papers on fraud detection, workflow automation, backend innovations, and infrastructure security. His work has informed backend improvements for institutions like TransUnion, and he’s played a role in refining ACH and wire transfer workflows at smaller U.S. banks.

His approach consistently centers on bringing AI and automation into areas that haven't traditionally seen much innovation, yet critically affect efficiency, security, and trust. He has also shown cross-domain expertise in AI-based fraud detection, robotic process automation, cloud-based infrastructure security, and scalable backend architecture, which helps him to explore the possibilities of AI applications.

One of the central areas where Garlapati has made an impact is ACH transfer automation. Traditionally, ACH transactions required layers of manual validation and reconciliation. By designing an end-to-end automation architecture, he helped reduce manual intervention by 65% and increased transaction processing by 30%. These changes freed up 40% of operations staff time, allowing teams to focus on strategic tasks instead of routine checks.

In parallel, his work in fraud detection has targeted real-time threats facing small and mid-size banks. Using long short-term memory (LSTM) models, AI tools well-suited for time-sequence data, he helped build fraud detection systems that reached up to 93% precision. These systems contributed to a 30% reduction in wire fraud incidents, offering scalable protection even for institutions without large IT budgets.

His contributions to performance optimization at TransUnion illustrate how foundational backend work translates into better user experiences. By identifying and resolving bottlenecks in data ingestion and synchronization, Garlapati helped enhance the speed and accuracy of credit report updates. The improved infrastructure didn't just mean faster reports; it supported better reporting accuracy, which impacts consumer lending decisions.

He's also taken on the growing challenge of cloud-based infrastructure security. Developed secure infrastructure strategies that leverage AI, encryption, and zero-trust architectures to protect against sophisticated cyber threats.

Among his projects stands the "Wire Transfer Fraud Detection" initiative, where he deployed a hybrid of supervised and unsupervised AI models for anomaly detection for U.S. banks. He also introduced generative adversarial networks (GANs) and graph neural networks (GNNs) to improve synthetic threat detection techniques that increased detection performance by over 18% compared to traditional methods. Further, he deployed RPA (software robots) to do repetitive, rule-based tasks in Banking Operations, where he conducted a critical evaluation of RPA’s impact on operational efficiency, identifying organizational and technical roadblocks and framing governance improvements.

Quantitatively, he's delivered over 95% accuracy with GAN-based deepfake detection models, increased recall for fraud-related anomalies, and driven a 50% reduction in manual errors across various automation projects.

Garlapati, while delivering solutions, also had to address challenges. One example is his work on RPA adoption in banking operations. In environments where automation often faces cultural pushback, he prioritized process mapping and governance frameworks that aligned with compliance expectations and successful deployment. He also overcame inefficiencies in data ingestion and synchronization at TransUnion, improving backend scalability and responsiveness and devised a low-cost AI fraud detection model scalable for small institutions with limited IT budgets.

His published works further reinforce his hands-on contributions. Titles like "Optimizing ACH Transfers Through Workflow Automation", "Reducing Wire Transfer Fraud Risks for U.S. Small Banks", “Addressing the Evolving Fraud Risks in Mobile and Digital Payments”, “Securing Cloud-Based Fraud Management Systems”, and “Revolutionizing Operational Efficiency in Banking” outline frameworks that he might have used in his professional commitments.

Looking ahead, Garlapati sees fraud prevention evolving into AI-first, real-time systems. Rule-based detection won't be enough. Instead, he advocates for AI models that can identify emerging threat patterns before they become incidents. Speaking of AI, automation in transaction processing (ACH, wire, real-time payments) will increasingly rely on AI-augmented decision-making and real-time monitoring dashboards, he tells.

On the infrastructure side, he sees a shift toward modular, cloud-native backends that are real-time, predictive, and secure by design.

Perhaps most critically, he emphasizes the human side of automation. As more banks introduce RPA and AI, Garlapati stresses the importance of retraining staff and redefining roles to ensure a sustainable and inclusive tech evolution in banking.

Whether it's credit infrastructure, payment security, or backend performance, Saikrishna Garlapati's work consistently addresses the overlooked systems that quietly power the banking experience. And in doing so, he's helping build a more resilient and intelligent financial infrastructure for the current and future.