AI Driven Cyber Deception in FinTech: An Adaptive Defense Strategy

Abstract

FinTech platforms clear high-value transactions in milliseconds, making them lucrative targets for adversaries who increasingly weaponize artificial intelligence. Once an attacker bypasses the perimeter, via credential stuffing, supply-chain malware, or deep-fake social engineering, traditional defenses often alert too late to prevent loss. We present an Adaptive Deception Defense Framework (ADDF) that intertwines AI-orchestrated honeypots, honeytokens and decoy micro-services within everyday banking and payment workflows. A recurrent-neural threat profiler classifies live attacker behavior; a Proximal-Policy-Optimization agent then selects actions such as spawning a shadow login API, cloning a database or injecting synthetic ledgers, thereby misdirecting intruders while harvesting telemetry. In a controlled “FinBank” test-bed featuring a vulnerable Flask-and-MySQL stack, ADDF shortened mean time-to-detect from 3 min 42 s to 29 s, increased attacker dwell-time inside decoys to 12 min 18 s, and prevented all real data exfiltration across ten attack trials. False-positive alerts remained below 1% per run, and added resource use averaged 14% CPU/RAM on mid-range servers. The framework also produced high-fidelity indicators of compromise, password lists, malware binaries and lateral-movement scripts, that would have been unavailable under baseline controls. These findings indicate that AI-driven cyber deception can transform FinTech security from passive monitoring into proactive engagement, mitigating breach impact while supplying rich threat intelligence. The paper details system architecture, reinforcement-learning policy training, empirical evaluation and operational implications—showing how defenders can regain initiative in the AI-to-AI cyber arms race without disrupting legitimate customers or breaching regulatory duties. FinTech platforms process high‑value transactions at internet speed, making them prime targets for advanced cyber‑criminals who now weaponize artificial intelligence. Traditional controls detect many incidents yet remain reactive; once adversaries bypass the perimeter, defenders struggle to contain damage fast enough to prevent data loss or fraud. We present an AI‑driven cyber‑deception framework that inserts a dynamic layer of honeypots, honeytokens and decoy services into a live FinTech environment. A learning engine classifies attacker behaviour in real time, then deploys or adapts decoys to misdirect adversaries while capturing rich telemetry. In a controlled banking testbed, the system cut mean time‑to‑detect from minutes to seconds, confined intruders to fake assets in every trial, and prevented exfiltration of real customer data. Adaptive deception also generated high‑quality threat intelligence with negligible false positives and modest resource overhead (<15% CPU/RAM on a mid‑range server). Findings from this study suggest that the use of AI-powered deception methods can shift or otherwise redirect Fintech defence posture from passive monitoring configurations to that of proactive engagement. This can reduce risk for defensive teams and potentially boost incident-response agility without significantly disrupting legitimate users within a system where the novelty lies in orchestrating established AI components (RL, anomaly classification, and generative decoys) into a closed-loop deception system for real-time FinTech operations.