Artificial Intelligence and Machine Learning: The Cyber Security Heroes Of FinTech
How AI and Machine Learning are Driving Cyber Security in FinTech?
Being a subset of the financial services domain, FinTech is targeted by hostile cyber villains. Industries thus require secure mechanisms to keep their data safe and secure. Preventing data losses are critical for Fintechs.
The World Economic Forum states that cyber-security is the Number One risk associated with the financial services industry.
Cyber Security Risks Associated With FinTech
Cybersecurity has remained a pressing concern for FinTech. Ever since the global financial crisis of 2008 that challenged the traditional financial institutions significantly, technology-driven start-ups have started evolving increasingly to cater to finance, risk management, digital investments, data security, and so on. Presently, we are in the FinTech 4.0 era.
The major cybersecurity risk that enterprises implementing FinTech face are from integration issues such as data privacy, legacy, compatibility, etc. Hackers target FinTech as they handle large volumes of customer data that include personal, financial, and other critical information.
FinTech offers a multitude of easily accessible services via its APIs. For instance, API banking. Here, the APIs are developed for the banks to access the FinTech platforms. It becomes open, API banking when open APIs enable third-party developers to build banking applications and services.
Let us walk through the major cybersecurity challenges triggered by FinTech.
Data Integrity Challenge
Mobile applications deployed for FinTech services play a predominant role in cybersecurity assurance. FinTech services require strong encryption algorithms to avoid integrity issues that can arise while transferring financial data.
Cloud Environment Security Challenge
Cloud computing services such as Payment Gateways, Digital Wallets including other secure online payment solutions are key enablers of the FinTech ecosystem. Though it is simple to make payments via cloud computing, it is equally crucial to maintain the security of data as far as banks are concerned. Appropriate cloud security measures are thus critical while dealing with sensitive information.
Third-Party Security Challenge
Third-party security challenges include data leakage, service challenges, litigation damages, and so on. Banks should be aware of FinTech service relationships while associating with third-parties.
Digital Identity Challenges
Major FinTech applications are web apps that have mobile devices working at the front-end. Banks and other financial institutions need to learn about the security architecture of the electronic banking services offered by these applications before implementing the FinTech application.
Money Laundering Challenges
The use of cryptocurrency for financial transactions makes FinTech-drive banks prone to money laundering activities. Thus, the FinTech ecosystem needs to be formally regulated based on global standards.
Private keys can be stolen in case of weak security in blockchain architecture. Cryptographic algorithms need to be strong and transactions need to be confidential.
The increase in the number of FinTech implementation of interfaces will cause a rise in the number of cybersecurity challenges as well.
How Artificial Intelligence And Machine Learning Enables Cyber Security For FinTech?
Artificial Intelligence is both reactive as well as proactive or preventative. AI reinvents FinTechs by bringing in behavioral biometrics solutions. These solutions are used to monitor customer and device interactions that take place during transactions that enhance security and authentication. BB or behavioral Biometrics with AI provides problem-solving capabilities for FinTechs. FinTechs utilize Artificial Intelligence is an expert system that enhances decision-making abilities using deductive reasoning. Big Data analytics is used here to focus on quality data.
The underlying technology in using Artificial Intelligence involves reasoning, learning, perception, problem solving, and linguistic intelligence to provide critical insights. It helps in understanding business in real-time operations.
In this digital era of increasing cybersecurity attacks and malpractices, AI can be used effectively to prevent risks and attacks. The following are major ways of how AI and ML protect FinTechs:
1. Fraud Detection
AI and machine learning algorithms are used to detect frauds in FinTechs by being able to identify transactions in real-time accurately. The traditional strategy of fraud detection involved analyzing large volumes of data against sets of defined rules using computers. This process was time-consuming and complex. Unlike this traditional method, more intelligent data analytics tools for fraud detection such as KDD (Knowledge Discovery In Databases), Pattern Recognition, Neural Networks, Machine Learning, Statistics, and Data Mining have evolved.
2. Controlling Access
Access control to critical data is crucial when it comes to security. Machine learning is used to derive critical insights from previous behavioral patterns such as geolocation, log-in time, etc to control access to endpoints. The risk scores are fine-tuned by combining supervised and unsupervised machine learning methods to reduce fraud and thwart breach attempts as well.
3. Smart Contracts
Smart contracts are coded in a programming language and stored on the blockchain. With blockchain, new contracts can be added to existing ones without having to change the individual contracts, in case of agreement expansion. Artificial Intelligence has become an integral part of FinTech as more traditional banks are teaming up with FinTechs to leverage the benefits of both worlds. For instance, when customers face issues with a poor credit history while applying for loans.
Artificial Intelligence is yet to be transforming the face of FinTechs in a multitude of ways. Drop-in a call right away and our strategists will guide you on how to leverage the benefits of AI and ML to secure operations and prevent breach attacks.