Artificial Intelligence (AI) is bringing a new era of FinTech by helping financial institutions manage risk, make smarter investment decisions, personalize customer experiences, and more. Reports suggest that 33% of financial institutions have already adopted AI in some form, and the remainder plans to use it in the next three years.
So what does this mean for you and your organization? Let's dive right in:
Applications of AI in the Finance Industry
There are multiple applications of AI in the financial industry. These include, but are not limited to:
AI is transforming fraud detection in finance by identifying suspicious patterns and behavior in real time. AI-powered systems use anomaly detection, natural language processing, and behavioral biometrics to analyze historical and unstructured data and predict the likelihood of fraud. Financial institutions can detect and prevent fraud more effectively and on time.
Two areas where AI has been very effective are:
Anomaly detection: AI systems can analyze historical data to identify unusual patterns or behaviors that may indicate fraud. For example, suppose a customer suddenly starts making a lot of large transactions from an unfamiliar location. In that case, an AI system might flag that activity as suspicious and trigger a fraud alert.
Behavioral biometrics: AI systems can analyze user behavior, such as typing speed and mouse movements, to detect potential fraud. For example, suppose a user suddenly starts typing much more slowly than usual. In that case, an AI system might flag that activity as suspicious and trigger a fraud alert.
AI has the potential to help investment management by improving the speed and accuracy of investment decisions. AI algorithms can optimize portfolios, manage risks, automate trading, and analyze sentiments.
The benefits of AI in investment management include improved decision-making, speed, efficiency, and risk management. With the growing use cases of AI in finance, it will likely boost the world economy by US$14 trillion by 2035
AI can manage risks in investment portfolios by using predictive analytics to identify potential threats, monitoring portfolios in real-time, conducting scenario analysis, and analyzing news articles and social media to identify trends. It can help investment managers make informed decisions, reduce losses, and identify opportunities.
However, AI systems are only as good as the data they are trained on. They may not anticipate unexpected events or external factors impacting investment performance. Therefore, AI risk management should be viewed as one tool in a broader risk management framework.
AI transforms trading by allowing traders to analyze vast data and make informed real-time decisions.
It is used in algorithmic trading to automate trading strategies, sentiment analysis to predict market reaction to the news, risk management to identify and mitigate potential risks, and high-frequency trading to take advantage of small market fluctuations.
Concerns With the Use of AI
The use of AI in finance also has many concerns on both technical and ethical grounds:
Concerns arise when AI algorithms require access to large amounts of customer data to make more informed decisions. As financial institutions gather more customer data, worries about how this data is being used, stored and secured become more prevalent.
Regulatory frameworks such as GDPR in Europe and CCPA in California have been implemented to ensure that customer data is collected, processed, and stored transparently and securely. Financial institutions must also ensure that they obtain consent from customers to use their data for specific purposes and comply with privacy laws and regulations.
It’s another concern related to the use of AI in finance. Bias can occur if the AI algorithms are trained on data not representative of the entire population, leading to decisions that favor one group over another.
For example, a machine learning algorithm trained on historical data that exclude certain demographic groups may learn to make decisions that disadvantage those groups. It can have profound implications, particularly in finance, where decisions based on biased algorithms could result in discrimination or unfair treatment.
Today, cybercriminals use sophisticated methods, including AI and artificial bots, bot farming, DDoS attacks, and more. Cybercriminals use adversarial AI to make ML models misinterpret inputs fed into the system and behave in a way that benefits the attackers.
The Anomaly Augmentation Attack is an example of how adversarial AI is used to mask anomalies in a company's financial accounts to bypass international audit standards and mislead investors regarding the financial state of a company.
AI has immense potential to transform the finance sector, but financial institutions must exercise caution and use it responsibly. Adequate safeguards are needed, and institutions must anticipate highly sophisticated AI-based attacks.
Our team's penetration testing capabilities can reveal the weak points in the system and offer actionable insights to leverage AI/ML to strengthen the security infrastructure. To learn more about our services and receive a free, zero-obligation quote, reach out today.