AI in Finance: Applications and Best Practices in 2024

As the banking landscape continues to evolve, the implementation of robotics and AI in security measures has become crucial. These technologies provide banks with robust tools to protect transactions, secure customer data, and stay one step ahead of cyber threats. By embracing robotics and AI, the banking sector can enhance security, build customer trust, and ensure a safe and reliable financial ecosystem for all. An AI-powered search engine for the finance industry, AlphaSense serves clients like banks, investment firms and Fortune 500 companies. The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets.

Issues such as complex risk assessment, slow customer service, and inefficient data processing are prevalent in the financial and banking sectors. ZBrain adeptly tackles these challenges with its specialized flows, which enable straightforward, no-code development of business logic for apps through an easy-to-use interface. It offers various large language models and templates to choose from, streamlining the creation and customization of intelligent applications.

Artificial Intelligence in Finance:

In addition, national AI strategies and policies have started to explicitly promote AI deployment in finance to build or leverage their country’s comparative advantage. Certain countries foster AI deployment in the sector as a priority sector in their national AI strategies. Additionally, regulatory bodies are using a variety of instruments to leverage AI and AI-powered innovation in finance while mitigating its risks.

For example, JPMorgan Chase’s CoiN technology reviews documents and derives data from them much faster than humans can. These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, and productivity and reduce costs. AI-based systems are now helping banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human. The right data partner will provide a range of security options, strong data protection through certifications and regulations, and security standards to ensure the customer data is handled appropriately.

Mitigating Financial Risks with Intelligent Algorithms

The report also found that proactive communication is rare with only 42% in banking, 49% for insurance, and 63% for wealth management. AI’s capacity for learning and adapting is one of the technology’s key advantages in cybersecurity. Financial institutions may keep one step ahead of cybercriminals by using machine learning algorithms, which can identify new attack patterns based on historical data. AI algorithms can identify high-risk customers and anomalous transactions by evaluating customer data, assisting institutions in more efficiently upholding their anti-money laundering (AML) duties. Yet, I’m finding that many financial institutions are holding back on investing in this incredible technology. Benefits like technological advancements, improved consumer acceptability, and altered regulatory frameworks help financial institutions decide to employ AI.

By analyzing vast data, generative AI enables virtual agents to offer personalized, tailored, and accurate responses, improving overall customer satisfaction. Generative AI-powered chatbots offer numerous benefits, reducing wait times, improving response times, and providing personalized interactions. They contribute to increased operational efficiency, handling a high volume of inquiries simultaneously and offering consistent, standardized responses.

Finally, if gen AI can be a powerful cybersecurity tool, it’s also true that criminals can exploit it, using it to produce “deep fakes” or churn out iterations of deceptive email copy in phishing expeditions. Security specialists will have to come to terms with this tech’s two-sided nature and stay a step ahead of bad actors. Generative AI can sometimes produce inaccurate or “hallucinated” information — a challenge for knowledge management.

AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service. Canoe ensures that alternate investments data, like documents on venture capital, art and antiques, hedge funds and commodities, can be collected and extracted efficiently. The company’s platform uses natural language processing, machine learning and meta-data analysis to verify and categorize a customer’s alternate investment documentation. Kensho, an S&P Global company, created machine learning training and data analytics software that can assess thousands of datasets and documents. Traders with access to Kensho’s AI-powered database in the days following Brexit used the information to quickly predict an extended drop in the British pound, Forbes reported.

When market conditions change, they can quickly alter the portfolio by reallocating assets to take advantage of opportunities or reduce risks. “AI-powered portfolio management systems continuously evaluate the trade-offs between risk and return for different asset pairings. Additionally, predictive models driven by AI have a high degree of accuracy in predicting market patterns and asset prices. This lessens the work required of compliance teams and guarantees continued compliance at financial institutions, lowering the possibility of regulatory fines and penalties.

AI for fintech equips companies with the capacity to detect and analyze threats in real-time. Machine learning algorithms sift through vast datasets to identify anomalous patterns or suspicious behavior, enabling early threat detection. The same technological advancements propelling this industry forward have also opened doors to new and sophisticated forms of cyber threats. Protecting sensitive financial data, ensuring regulatory compliance , and maintaining trust in the digital financial ecosystem is crucial for security in fintech. AI systems’ ability to handle big data and analyze it smartly has found an application in Robo-advising and investment management. Robots of Betterment and Ellevest help clients make investment decisions based on large-scale stock dynamics data.

Ethical considerations and challenges of generative AI in the finance industry

With the right storage, organizations can accelerate generative AI (discussed in more detail here). Excellent customer service is increasingly valued as a key competency to keep customers engaged and satisfied. However, relying on live agents for immediate, round-the-clock support is impractical and costly. It is necessary to create strong standards and Secure AI for Finance Organizations frameworks for algorithmic accountability, transparency, and fairness in Exploring Ethical Considerations in AI. Data governance procedures are used to guarantee varied and representative information sets. AI systems are routinely checked for biases, and regulatory monitoring is used to encourage ethical and equitable usage of AI technology in finance.

In conclusion, robotics and AI are set to play a pivotal role in shaping the future of the banking industry. Embracing these technologies can empower financial institutions to streamline operations, enhance customer experiences, and stay ahead of the competition. By preparing for the future today, banks can position themselves as leaders in the digital era. As AI systems are able to identify anomalies and discrepancies in financial transactions in real time, they can become a powerful tool for any organization’s compliance tracking process. The detected non-compliant transactions are quickly blocked, preventing the company from perpetuating fraud and illicit activities. This way, AI can help financial entities preserve their reputation, ensure regulatory compliance, avoid fines, and protect user funds from theft.