Artificial Intelligence in Financial Services 2023

Plaid works as a widget that connects a bank with the client’s app to ensure secure financial transactions. Every day, huge quantities of digital transactions take place as users move money, pay bills, deposit checks and trade stocks online. The need to ramp up cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution, and AI plays a key role in improving the security of online finance. For Chase, consumer banking represents over 50% of its net income; as such, the bank has adopted key fraud detecting applications for its account holders.

Agents can access details of buy and sell orders in near-real time for faster problem resolution and shorter settlement periods. In cases of credit decisions, this also includes information on factors, including personal data that have influenced the applicant’s credit scoring. In certain jurisdictions, such as Poland, information should also be provided to the applicant on measures that the applicant can take to improve their creditworthiness. Although many countries have dedicated AI strategies (OECD, 2019[52]), a very small number of jurisdictions have current requirements that are specifically targeting AI-based algorithms and models. In most cases, regulation and supervision of ML applications are based on overarching requirements for systems and controls (IOSCO, 2020[39]). These consist primarily of rigorous testing of the algorithms used before they are deployed in the market, and continuous monitoring of their performance throughout their lifecycle.

AI in Finance

Demand for employees with applicable skills in AI methods, advanced mathematics, software engineering and data science is rising, while the application of such technologies may result in potentially significant job losses across the industry (Noonan, 1998[54]) (US Treasury, 2018[32]). Such loss of jobs replaced by machines may result in an over-reliance in fully automated AI systems, which could, in turn, lead to increased risk of disruption of service with potential systemic impact in the markets. The implementation of AI applications in blockchain systems is currently concentrated in use-cases related to risk management, detection of fraud and compliance processes, including through the introduction of automated restrictions to a network. AI can be used to reduce (but not eliminate) security susceptibilities and help protect against compromising of the network, for example in payment applications, by identifying irregular activities for instance..

Asset managers and the buy-side of the market have used AI for a number of years already, mainly for portfolio allocation, but also to strengthen risk management and back-office operations. AI is also used by asset managers and other institutional investors to enhance risk management, as ML allow for the cost-effective monitoring of thousands of risk parameters on a daily basis, and for the simulation of portfolio performance under thousands of market/economic scenarios. This section looks at how AI and big data can influence the business models and activities of financial firms in the areas of asset management and investing; trading; lending; and blockchain applications in finance. The firms will train their own foundation models using their own company data, allowing banks to build models that perform more accurately and deliver a more personalized experience to customers, Levitt says.

This is best demonstrated by solutions from key Intel partners, Aerospike and MemVerge, that leverage 2nd Generation Intel® Optane™ technology to enable the real-time storage and analysis that is required in the trading industry. The predictions for stock performance are more accurate, due to the fact that algorithms can test trading systems based on past data and bring the validation process to a whole new level before pushing it live. AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern. For a number of years now, artificial intelligence has been very successful in battling financial fraud — and the future is looking brighter every year, as machine learning is catching up with the criminals.

The key role of cloud-based infrastructure and accelerated processing

These are all steps that will lead to a world where Sally can have instant access to a potential mortgage. In a world where generative AI tools can permeate a bank, Sally should be continuously underwritten so that the moment she decides to buy a home, she has a pre-approved mortgage. Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities. Despite feeling like things are moving too fast, 71 percent of FS business leaders say they think the U.S. is behind in terms of AI adoption.

However, with the help of AI, online casinos can now offer Fast And Easy Withdrawals to their players. AI algorithms can analyze and verify user data in real-time, reducing the time it takes to process withdrawals. What if you could spend less time pulling together industry and company analyses, and more on delivering the best investment guidance for clients?

AI for financial markets

Intel’s products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right. As processing power continues to scale, high-bandwidth access to low-latency storage is critical to realizing the true potential of the CPU. Intel® VMD and Intel® VROC are features of Intel® Xeon® Scalable processors that enable a seamless transition to fast NVMe storage to maximize CPU access to data, without a disruption in storage functionality. It helps businesses raise capital and handle automated marketing and messaging and uses blockchain to check investor referral and suitability. Additionally, Wealthblock’s AI automates content and keeps investors continuously engaged throughout the process.

These intelligent systems track income, essential recurring expenses, and spending habits and come up with an optimized plan and financial tips. Artificial intelligence truly shines when it comes to exploring new ways to provide additional benefits and comfort to individual users. Data-driven investments have been rising steadily over the last 5 years and closed in on a trillion dollars in 2018. Another bright example of using AI is education where open online courses (MOOC) such as Coursera or Lynda become more and more popular each year. Automatic grading made self-taught online courses available for anyone with Internet access — a pivotal point for so many lives and careers. Here are a few examples of companies using AI and blockchain to raise capital, manage crypto and more.

Explore the technology

That said, there is no formal requirement for explainability for human-initiated trading strategies, although the rational underpinning these can be easily expressed by the trader involved. Leading banks and financial institutions are using AI-powered technologies, such as natural language processing, to reimagine customer service and interactions. To remove service bottlenecks, solutions like robo-advisors can answer tough questions in milliseconds. Finally, Intel has been working with financial services companies for decades to help them address their most complex challenges. As a leading technology innovator, Intel serves as a trusted partner to financial services institutions that are interested in deploying artificial intelligence within their organizations.

Nevertheless, it should be noted that AI-based credit scoring models remain untested over longer credit cycles or in case of a market downturn. That explains why artificial intelligence is already gaining broad adoption in the financial services industry with the use of chatbots, machine learning algorithms, and in other ways. Other fintech companies are also embracing AI as a way to differentiate themselves from legacy institutions like banks, and even banks have embraced artificial intelligence for things like customer service, fraud detection, and analyzing market data. Unsurprisingly, popular use cases include AI risk management and compliance, fraud prevention and detection for insurance, identity and anti-money laundering (AML).

Inadequately designed and controlled AI/ML models carry a risk of exacerbating or reinforcing existing biases while at the same time making discrimination even harder to observe (Klein, 2020[35]). Auditing mechanisms of the model and the algorithm that sense check the results of the model against baseline datasets can help ensure that there is no unfair treatment or discrimination by the technology. Ideally, users and supervisors should be able to test scoring systems to ensure their fairness and accuracy (Citron and Pasquale, 2014[23]). Tests can also be run based on whether protected classes can be inferred from other attributes in the data, and a number of techniques can be applied to identify and/or rectify discrimination in ML models (Feldman et al., 2015[36]).

AI has the ability to analyze and single-out irregularities in patterns that would otherwise go unnoticed by humans. H2O.ai has grown alongside financial services firms, who are not only our biggest customers but also our biggest investors, and it is evident in the platform today. Our host of capabilities are designed your bank statement to allow companies to build fast, while improving transparency and trust. In spite of the dynamic nature of AI models and their evolution through learning from new data, they may not be able to perform under idiosyncratic one-time events not reflected in the data used to train the model, such as the COVID-19 pandemic.

See the STAC-A2™ benchmark results for systems using 2nd and 3rd generation Intel® Xeon® Scalable processors to support market risk analysis workloads. Intel® technologies and Intel-powered solutions are helping organizations accelerate analytics efforts across the data pipeline. A leading financial firm, JP Morgan Chase, has been successfully leveraging Robotic Process Automation (RPA) for a while now to perform tasks such as extracting data, comply with Know Your Customer regulations, and capture documents. RPA is one of ‘five emerging technologies‘ JP Morgan Chase uses to enhance the cash management process. Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement. Ernst & Young has reported a 50%-70% cost reduction for these kinds of tasks, and Forbes calls it a “Gateway Drug To Digital Transformation”.

• Kagoo said organizations that approach AI from just a defensive strategy—to use AI and machine learning to drive cost savings, or to drive operational efficiency — are not yet realizing the returns.
• Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report.
• Unsurprisingly, popular use cases include AI risk management and compliance, fraud prevention and detection for insurance, identity and anti-money laundering (AML).
• Banks use AI for customer service in a wide range of activities, including receiving queries through a chatbot or a voice recognition application.
• It may be smart to consider investing in one of these artificial intelligence-oriented ETFs.

Amidst volatile industry and global conditions, researchers say a diverse array of firms in the sector are adopting AI in record numbers to seize new opportunities. IT and business leaders aim to drive data-driven innovations that will improve customer experience, reduce risk and fraud while creating operational efficiencies and reducing costs. Ayasdi creates cloud-based machine intelligence solutions for fintech businesses and organizations to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes. Its Sensa AML and fraud detection software runs continuous integration and deployment and analyzes its own as well as third-party data to identify and weed out false positives and detect new fraud activity. Generative AI has the potential to revolutionize financial services specifically in the payments, banking, and insurance sectors.