Significance of Data Structures and Algorithms in Financial Technology

Authors: Ashmitha Nagraj

DOI: https://doi.org/10.37082/IJIRMPS.v8.i2.232957

Short DOI: https://doi.org/

Country: United States

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Abstract: Financial Technology (FinTech) has transformed Capital Markets, Payments, Lending and Risk Management through faster processing of financial decisions in real-time and expanding access to financial services digitally. Beneath these user-friendly applications, FinTech platforms rely on foundational Data Structures and Algorithms to provide consistent Latency, Scalable Throughput, Auditing capabilities, and Resilience under adversarial conditions. The purpose of this paper is to review which core Data Structures (Arrays, Linked Lists, Hash Tables, Balanced Trees, Heaps and Graphs) are used to support key FinTech Workload applications (Algorithmic Trading, Fraud Detection, Credit Risk Assessment and Blockchain-based Recordkeeping). In addition, this paper will review the Algorithmic Foundations used to enable common tasks across all these workload applications including Sorting/Searching, Optimization, Statistical Learning and Cryptography, and how Asymptotic Complexity must be evaluated with respect to practical system constraints including Caching Behavior, Concurrency and Failure Modes. There is evidence from the academic literature that Algorithmic Trading can increase liquidity in certain Market Structures while also introducing Systemic Fragility during Stress [1],[2],[3]. And, similarly, there is evidence that Fraud Detection is an inherently adversarial domain where Models and Features must evolve as Attacker Behavior evolves [4],[5]. Lastly, the Paper will discuss several open challenges associated with Scale, Security, Model Governance and Privacy; and evaluate Future-Facing Directions such as Privacy-Preserving Analytics (Federated Learning and Zero-Knowledge Proofs) and Cryptographic Agility to prepare for post-Quantum risk.

Keywords: FinTech, Data Structures and Algorithms, Algorithmic Trading, Fraud Detection, Credit Risk Assessment, Blockchain Systems, Time and Space Complexity, Scalable Systems, Low-Latency Computing, Adversarial Machine Learning, Cryptographic Systems, Model Governance, Privacy-Preserving Analytics, Federated Learning, Post-Quantum Cryptography

Paper Id: 232957

Published On: 2020-03-25

Published In: Volume 8, Issue 2, March-April 2020

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