AI, trust, and data security are key issues for finance firms and their customers

Automation and efficiency in finance refer to the application of artificial intelligence (AI) technologies to automate tasks, lessen manual labor, and boost operational effectiveness. Automation involves integrating robotic process automation (RPA) and AI methods for automating routine tasks, increasing productivity, and optimizing workflows. The importance of Personalized financial services lies in their ability to deliver more relevant and tailored offerings to customers which increases customer pleasure, engagement, and loyalty.

• Given the sensitive nature of data and high-value transactions, the banking industry and other financial services grapple with significant cybersecurity challenges.
• Despite these challenges, the potential benefits of generative AI in finance and banking far outweigh the limitations, making it a promising and transformative force in the industry.
• Additionally, fraud detection systems should be constantly monitored for fairness, security, transparency and explainability.
• Plaid works as a widget that connects a bank with the client’s app to ensure secure financial transactions.
• According to a recent survey, more than 85% of IT executives in banking already have a “clear strategy” for the adoption of AI in the development of their new products and services.

The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades. Time is money in the finance world, but risk can be deadly if not given the proper attention. Socure created ID+ Platform, an identity verification system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately.

Money Laundering Security and Fraud Detection

No wonder more than a third of financial service companies have jumped on the AI bandwagon recently. Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. KAI helps banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions. Salient policy considerations for the use of AI in finance pertain to inclusion and broadening access to financial services, while mitigating bias and digital security risks. Transparency and explainability of AI systems in finance are also key to allow people to understand and as appropriate, to challenge the outcomes of AI systems and to enable regulatory oversight.

The finance industry has always seen the potential benefits of implementing AI-based solutions. But with the widespread impact of COVID-19, AI has become more of a necessity rather than an option. Most people have embraced the digital experience, and the paradigm shift from traditional banking channels to virtual AI-based services is now more critical than ever. Industries that are extensively involved in e-commerce have transitioned from rule-based systems to machine learning-based models. AI-based fraud detection technologies can constantly adjust rules and even learn new ones as more and more data is processed. Wells Fargo’s predictive banking feature is an AI-powered enhancement to their mobile app that provides personalized account insights and delivers tailored guidance based on customer data.

Cybersecurity compliance made simple

The technology also personalizes the customer experience for each unique customer’s needs. For example, Bank of America’s virtual assistant Erica recently reached the milestone of over a billion client interactions since launching in 2018, with nearly 1.5 million per day. AI is evolving into the linchpin of the embedded finance revolution, thanks to advancements in generative AI (GenAI), large language models (LLMs), and deep learning. LLMs can handle customer queries, manage data, and predict market trends with greater accuracy (see Figure 1).

For example, DORA requires continuous monitoring and control of the security and functioning of ICT systems, with ultimate responsibility and accountability for compliance placed on the financial services firm’s management body. Regulatory sandboxes are increasingly being leveraged in the financial sector to this effect (see section 1.4.2). Labour market policies are also important to reskill and upskill finance practitioners, regulators and supervisors to adapt to new technologies and practices enabled by AI diffusion (Principle 2.4; see chapter 5). In addition to being grounded in specific values, fostering the development and deployment of trustworthy AI calls for the design and implementation of tailored policies in various areas.

Banks are experiencing more destructive cyberattacks — those that result in deleted data, damaged hard drives, disrupted network connections or leave some other trail of digital wreckage in their wake. In fact, 63 percent of financial institutions say they’ve experienced an increase in destructive attacks targeting their organizations. Fortunately, as cyberattacks continue to become more prevalent and sophisticated, artificial intelligence continues to evolve as a tool to help security professionals stay ahead of threats. Here are three ways that deploying AI can help financial institutions bolster their security.

This empowers financial professionals to make informed, data-driven decisions faster, resulting in improved outcomes and performance. Leveraging advanced algorithms and machine learning models, financial professionals can elevate their decision-making capabilities with AI as their ally. It doesn’t surprise that AI has helped organizations boost revenues by streamlining programs and procedures, automating repetitive jobs, and improving customer service. According to a Business Insider report, artificial intelligence technologies will likely save banks and financial organizations $447 billion by 2023. Additionally, around 80 percent of banks see AI’s potential benefits, and with the wider impact of COVID-19, which impacted the banking industry and drove more consumers to adopt the digital experience, it’s more vital than ever. Robust governance is seen as a necessary pillar in the safe adoption of AI in the financial services sector.

The Pros and Cons of Artificial Intelligence in the Financial Services Industry

Artificial intelligence (AI) can distinguish between valid and suspect activity by examining transaction histories and client patterns. Through security orchestration, automation and response solutions, AI can help financial institutions do just that. SOAR uses AI and machine learning to connect security tools and integrate disparate security systems, consolidating threat Secure AI for Finance Organizations alerts and enabling security automation. While AI offers numerous benefits to financial professionals, organizations must consider the serious issue of keeping sensitive financial information safe. Particularly where financial information could be impacting public markets, data has to be carefully handled and distributed publicly in a way that is responsible.

The feature is built on an ML algorithm that, for example, rounds up the price of a latte from $3.65 to, say, $3.90 and deposits the extra 25 cents—the amounts saved are all based on a given customer’s financial habits and ability. Generative AI’s ability to analyze large datasets, recognize patterns, and make informed decisions renders it invaluable in these applications. Therefore, AI-driven capital generation requires careful regulation and governance to ensure its ethical and responsible use. It also requires collaboration and coordination among policymakers, regulators, industry players, researchers, and consumers to foster innovation and trust.

II. Use cases of generative AI in finance

AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect. AI and ML in banking use deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process. Even though AI in the banking sector can’t replace compliance analysts, it can make their operations faster and more efficient. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults.

Tailoring models trained with proprietary data through techniques such as fine tuning, prompt tuning, and discovery has emerged as a key agenda. Based on individual customer profiles and preferences, these trained AIs are now able to provide personalized financial advice and guidance and assist users with budgeting, financial planning, and investment decisions. In one report, 72% of financial services companies surveyed said they were adopting AI to increase revenue. This is because with increased efficiency, financial institutions can reduce costs and increase profits. Financial institutions havemuch to gain by adopting AI to improve revenue and reduce costs. McKinsey, aglobal consulting firm, estimates that AI could deliver up to $1 trillion in value to global banks annually.

AI and blockchain are both used across nearly all industries — but they work especially well together. AI’s ability to rapidly and comprehensively read and correlate data combined with blockchain’s digital recording capabilities allows for more transparency and enhanced security in finance. AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets.

In conclusion, integrating generative AI in finance offers a transformative pathway toward enhanced efficiency, informed decision-making, and personalized customer experiences. As generative AI continues to mature, its potential benefits in risk assessment, fraud detection, investment management, and customer engagement are becoming increasingly evident. Analysis of historical market data enables real-time, adaptable trading strategies, ensuring swift responses to market changes for superior outcomes. Additionally, generative AI aids risk assessment, providing insights from complex market trends and economic indicators. This trait enhances bankers’ informed investment decisions and boosts portfolio risk-adjusted returns. Additionally, Kim et al. utilized CTAB-GAN, a conditional GAN-based tabular data generator, to generate synthetic data for credit card transactions, outperforming previous approaches.

That means a lot of extra attention, new clients, and better conditions for the current ones. One of the common problems in trading is getting market analysis too late to take advantage of opportunities. AI finance tools can outperform human trades and bring faster and better decisions on trading. Also, the comprehensive analysis of different market aspects and factors allows banks to achieve new heights in trading algorithms.

Founded in 2009, BairesDev is the leading nearshore technology solutions company, with 4,000+ professionals in more than 50 countries, representing the top 1% of tech talent. The company’s goal is to create lasting value throughout the entire digital transformation journey. In addition to this, we provide an automated model training pipeline to retrain the model on a yearly basis, and reports on the results of ‘Bics’ runs are also automatically generated and provided to corporate credit officers. It’s critical to comprehend and manage shifting workforce dynamics to enable a smooth transition and assist staff in adjusting to the changing environment. Financial providers need to address the potential effects on employment, support upgrading programs, and provide chances for staff to use their knowledge of AI technology in tandem with one another. Malicious actors who want to influence the inputs or outputs of AI systems emerge and inject biases or inaccuracies into trading decisions.

That echoed the Executive Order, entitled “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence,” which specifically calls out financial services, and requires the U.S. Treasury to issue a public report on best practices for financial institutions to manage AI-specific cybersecurity risks within 150 days of the Executive Order. Additionally, fraud detection systems should be constantly monitored for fairness, security, transparency and explainability. Issues identified should be corrected by the AI actors involved at the relevant lifecycle phase (including data collectors, developers, modellers, and system integrators and operators).