Artificial Intelligence (AI) is rapidly transforming financial services. According to The Bank of England, 75% of financial services firms are already using AI. A further 10% are planning to use it in the next three years.
Firms are deploying AI because of the benefits it can bring. These include enhanced data and analytical insights, improved anti-money laundering (AML) and fraud detection and efficiencies in cybersecurity practices. As well as providing customers with better, more personalised services.
While the wide-scale deployment of AI brings a range of benefits for the financial services sector, it’s also creating additional risks. Especially when the AI systems used to make trusted decisions are becoming a prime target for cyber-attacks.
Attacking AI
Bad actors can manipulate AI systems to make them malfunction or operate in ways that weren’t intended. This can have potentially severe consequences.
Using what’s known as data poisoning attack, threat actors can intentionally compromise or alter datasets used by AI to influence the outcomes of the model for their own malicious ends.
For example, an attacker trying to bypass the AI-powered fraud detection systems of a bank could attempt to inject false data into the system during a data training cycle. the intention would be to manipulate the system into believing certain false transactions are legitimate. Ultimately this enables the threat actor to steal money or sensitive data without being noticed.
AI systems can also result in additional threats to data privacy. Like many workers, financial service professionals can use Large Language Models (LLMs) like ChatGPT to aid with queries and tasks.
However, this brings the risk that sensitive information could get uploaded to the model if the employee inputs certain data, such as contracts or confidential reports. This data might be saved by the model, opening businesses up to data leaks. Because with the correct prompts, it’s possible for a user from outside the company to tease out this confidential information from the LLM.
These privacy concerns can be exacerbated by the black box nature of AI. Often, it isn’t publicly detailed how the algorithms and the decision-making process behind them operate. This lack of transparency can lead to mistrust among users and stakeholders. As well as potential issues with regulatory compliance. For example, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
All of this means that the use of AI in financial services, while beneficial, is creating new security challenges which need to be addressed. The solution to this is the integration of blockchain technology to create a secure, transparent, and trustworthy AI ecosystem. And by leveraging blockchain’s inherent security features, vulnerabilities in AI systems can be countered.
Blockchain Explained
Blockchain consists of a chain of blocks, each containing a list of transactions. Each block is linked to the previous one, forming a secure chain. This structure ensures that once data is recorded, it cannot be altered without changing all subsequent blocks. These mechanisms ensure that all participants agree on the state of the blockchain. Therefore preventing fraud and enhancing security.
This is achieved through three key pillars. The first is data immutability, which ensures it can’t be altered or deleted once recorded on the blockchain. Guaranteeing that the data remains consistent and trustworthy over time, ensuring its integrity.
The second pillar is decentralisation, based on how blockchain functions through a network of independent nodes. Unlike centralised systems, where a single point of failure can compromise the entire network, decentralisation distributes control and data across many nodes. This reduces the risk of system failures, as no single target point exists, meaning decentralisation enhances security and resilience.
Cryptographic security is the third pillar. Blockchain uses a system of public and private keys to secure transactions and control access. The public key is visible to anyone, while the private key is a secret code known only to the authorised party.
These fundamentals of blockchain, combined with the transparency and security it offers, can help financial services organisations address the security challenges they’re being faced with by the rapid deployment of AI.
Combining Blockchain with AI for Improved Data Security
Integrating blockchain with AI can massively aid with securing data integrity. For example, through creating tamper-proof records. By making immutable records of AI training data and model updates, complete with timestamps and links to previous entries, this ensures a tamper-proof history of the data. Enabling stakeholders at financial services companies to verify the integrity of the data used in AI models. Therefore improving security of the whole system and protecting it against attacks.
Combining AI with blockchain can also help to counter potential data privacy implications introduced by the deployment of AI in financial services. Blockchain techniques like zero-knowledge proofs allow the data to be verified without revealing the actual data. This can help financial services firms to verify the data they’re using is correct. While also still maintaining the required data privacy and regulatory compliance.
In addition to this, implementing AI with blockchain technology can aid with building trust and transparency in how AI systems work and what they’re used for. By providing a transparent record of AI decision-making processes, the blockchain allows stakeholders to review and verify the process. All the while ensuring there’s accountability of who made changes and when. This arrangement could therefore help financial services providers prevent data poisoning and other attacks targeting their AI systems.
Building a Secure, Transparent, and Trustworthy AI Ecosystem
The rapid adoption of AI is changing the financial services industry. However, according to The Bank of England’s survey, only 34% of financial services firms said they have ‘complete understanding’ of the AI technologies they use.
Much of this can be attributed to how the technology is new, but also how the algorithms which power AI technology are often mysterious in their nature. This results in risks around malicious attacks and data privacy issues. However, by combining AI frameworks with blockchain technology, these security issues can be addressed.
By taking these steps, stakeholders can collectively contribute to building a secure, transparent, and trustworthy AI ecosystem. An ecosytem that leverages the strengths of blockchain technology to address current and future challenges.
- Artificial Intelligence in FinTech
- Blockchain