AI Risk Management for Financial Industry

AI risk management for financial industry

AI benefits of the finance industry

Artificial intelligence (AI) has made significant advancements in recent years, transforming various sectors, including the financial one. With its ability to analyze vast amounts of data, detect patterns, and automate processes, AI offers numerous benefits to banking. These advantages include enhanced efficiency, improved risk management, personalized customer experiences, fraud detection, and cost savings.

AI algorithms can quickly process complex financial data, enabling faster decision-making and reducing manual errors. Additionally, AI-powered chatbots and virtual assistants provide customers with personalized recommendations and support.

The flip side: AI risks

Despite its numerous benefits, the use of AI in the financial industry also presents several risks that need careful consideration.

Here are five key risks associated with AI in financial industries.

Inaccurate verification

Attackers cannot but exploit security vulnerabilities in the process of verifying the identity. As a result, facial recognition systems can misidentify and approve a malefactor instead of a real client and let them access the finances of others.

Fraud detection failure

Attackers can trick anti-fraud systems. This can be done in case of stealth evasion attacks and may result in fraud.

Cybersecurity vulnerabilities

AI systems often handle sensitive financial information, making them attractive targets for cybercriminals. Hackers may attempt to manipulate AI models, inject malicious data, or exploit vulnerabilities in AI algorithms to gain unauthorized access to financial systems. These cybersecurity risks pose significant threats to financial institutions and their customers.

LLMs and AI Chatbots

AI language models and chatbots such as GPT, Claude are vulnerable to various attacks: prompt injections, jailbreaks, data stealing, adversarial examples, and other safety bypass techniques.

Regulatory Compliance and legal issues

The use of AI in financial industries raises legal and regulatory challenges. Regulators must ensure that AI systems comply with existing regulations, such as anti-money laundering (AML) and know-your-customer (KYC) requirements. The complexity of AI systems, coupled with potential biases and lack of explainability, can make it difficult to establish legal accountability and meet regulatory standards.

AI incidents

Cases where the use of AI in the financial industry result in financial losses or insecure practices.

In 2018, researchers demonstrated a proof-of-concept using Siri and Microsoft’s Speaker Recognition API, synthesized the target’s voice and were able to successfully authenticate with a Windows service. It was a raspy sounding voice with all the necessary to trick authentication.

“Many global banks and global insurance companies are looking forward for let’s say, three to five years, to understand what could be the threat if some of the algorithms or encryption will be broken,” Fabio Colombo, global lead for financial services security at consultancy Accenture, says, urging banks to assess where they might be vulnerable and how they might be able to address any potential compromises.

by Hannah Murphy, Financial Times

How we can help with AI risk management for Insurance

Our team of security professionals has deep knowledge and considerable skills in cyber security, AI algorithms, and models that underlie any content moderation system. Your algorithms can be tested against the most critical AI vulnerability categories that include Evasion, Poisoning, Inference, Trojans, Backdoors, and others.

We offer Solutions for Awareness, Assessment, and Assurance areas to provide 360-degree end-to-end visibility on the AI threat landscape.

Secure AI Awareness to demonstrate AI risks and shape AI governance strategy. It consists of Policy Checkup, AI Risks Training Threat Intelligence for informed decisions.
Secure AI Assessment helps to perform AI integrity validation and identify AI vulnerabilities through Threat Modeling, Vulnerability Audit, and automated AI Red Teaming.
Secure AI Assurance helps to remediate AI risks and implement a lifecycle for AI integrity. It consists of Security Evaluation, Risk Mitigation, and Attack Detection.

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Adversa AI is a first mover in commercializing security technologies for AI with patent-pending technology.

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