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How Has The Fintech Sector Been Impacted By AI And Cybersecurity

The FinTech industry is distinguished by its high degree of innovation within a complex ecosystem that includes, among others, banks, financial service providers, and start-ups.

It’s no longer a miracle. AI is progressively being interwoven into the fabric of business and broadly used across various application use cases.

According to a recent global study of over 4,500 technology decision-makers from various industries, 45 percent of major enterprises and 29 percent of SMEs had adopted AI.

The FinTech industry is distinguished by its high degree of innovation within a complex ecosystem that includes, among others, banks, financial service providers, and start-ups.

Not surprisingly, then, in recent years, it has relied heavily on artificial intelligence (AI) and machine learning (ML) for strategic decision making, customer insights, understanding consumer purchasing behaviour, and improving the digital transaction experience.

However, financial technologies in the FinTech ecosystem are powered by large volumes of data, which along with the weaknesses inherent in new technologies, makes the industry an appealing target for cybercriminals. The industry poses unique threats not just to data subjects’ rights and freedoms, but also to their financial holdings and, perhaps, their economic well-being.

The interplay of AI and cybersecurity

Both AI and machine learning have the potential to simplify transactions and revamp the entire consumer experience. But, aside from improving the user experience, these two technologies are also helping to boost cybersecurity and avoid vulnerabilities through proactive processes and measures.

The relationship of AI and cybersecurity in the banking and financial industry can be broadly separated into two dimensions:

Offensive AI usage:

In this element, cyber attackers utilise AI to discover and detect passwords or user credentials, as well as to exploit photos and audio, which can be further used to conduct identity fraud or theft, undertake more genuine phishing attacks, or even generate new kinds of malware.

This calls for cybersecurity to both prevent abuse (e.g., hacking or manipulation of the AI algorithms or manipulation of the data processed by the AI algorithm) and include mechanisms to ensure consumer safety and effective reparation to victims in the event of injury, as well as to mediate investigations if the AI system is compromised.

As an example, the European Commission has proposed that cybersecurity requirements for AI be codified under the proposed European Cybersecurity Certification Framework. It has noted that for “businesses acting in security relevant fields (e.g. financial institutions, producers of radio-active materials, etc.) the use of certain AI products and processes serves public interest and thus their use may be made mandatory.”

But building confidence in AI requires a sufficient safety and responsibility framework.

Hence, the second dimension:

Defensive AI usage:

Here, AI is utilised to combat cyber-attacks:

As the number and complexity of cyberattacks increase, artificial intelligence (AI) is assisting under-resourced security management strategists in staying abreast of the latest developments.

Diagnostic Capabilities

AI technologies like machine learning and natural language processing, which curate security intelligence from millions of research studies, articles, and news headlines, offer rapid insights to cut through all the clamour of everyday alarm, creating significant diagnostic capabilities that analyse massive quantities of information and highlight hidden patterns and anomalies.

Processing Sensitive Financial Data

Thanks to its ability to discern patterns and suspect behaviours, artificial intelligence is being used to spot fraudulent activities, questionable transactions, and generally offer a boost to processing sensitive financial documentation – all with a lower chance of security risk.

This is especially important for central banks, where the flexibility and real-time availability of big data provides the option of extracting more immediate economic signals, using new statistical approaches, improving economic predictions and financial stability assessments, and gaining quick feedback on policy implications, but where public statisticians have a propensity to be “cloud computing-averse,” owing to the exposure concerns presented for confidential private data.

Tailored customer segmentation

By evaluating cyber-secured client data to develop more targeted, multiple customer segments for financial companies, AI overcomes the constraints of traditional market segmentation. It goes a step further by automatically tailoring campaigns to each transaction category and fine-tunes marketing efforts for each channel by adjusting the factors involved. This is a win-win situation for both customers who get targeted with valuable offers and for banks who get higher conversions.

While the development of new malware tactics is not going away, there are immediate lessons that can be learnt and proactive actions that enterprises can take to maintain their operations’ resilience against cybersecurity threats. In the future, the use of suitable cyber protection and data security solutions will be crucial for FinTech business models to overcome risk issues, comply with government regulations, and win client trust.

But to build this future, the moment to act is now!

Source: Business World

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