AI in banking: Can banks meet the challenge?

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Said they believed that the technology will fundamentally change the way they do business. The pressing questions for banking institutions are how and where to use gen AI most effectively, and how to ensure the applications are fully adopted and scaled within their organizations. It is uncertain if, how, and when, a global standard for AI risk management will emerge (as it did with GDPR for data protection). Various approaches are being tested with some focusing on individual rights and others on overall AI safety. As a result, global financial firms implementing AI must develop a compliance and risk management strategy balancing local specificity and global consistency while adapting to evolving international rules and regulations.

So it is possible that at some point in the future, it will be cheaper and easier for firms to build a proprietary LLM. Developing a proprietary LLM is expensive because it requires lots of raw computing power to crunch the vehicle wrap advertising statistics data and it necessitates attracting and retaining highly specialized engineering talent with experience building LLMs from scratch. The second option is to use an open source LLM, such as Meta’s Llama 2, Mosaic or Falcon.

  1. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions.
  2. But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality.
  3. Earlier deployments of automated tools have also faced controversy over the impact of their failures, such as wrongful arrests in the US because of the limitations of facial recognition technology.
  4. Here are a few examples of companies using AI and blockchain to raise capital, manage crypto and more.
  5. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015,
  6. Moreover, the complexity of these technologies is causing many financial services firms to rely on third-party providers to support the implementation of these applications.

As this monumental shift unfolds, financial services professionals grapple with both the promising advantages and the challenges that come hand-in-hand with this technology. While implementing and scaling up gen AI capabilities can present complex challenges in areas including model tuning and data quality, the process can be easier and more straightforward than a traditional AI project of similar scope. Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms.

This approach helped frontrunners look at innovative ways to utilize AI for achieving diverse business opportunities, which has started to bear fruit. It is no surprise, then, that one in two respondents were looking to achieve cost savings or productivity gains from their AI investments. Indeed, in addition to more qualitative goals, AI solutions are often meant to automate labor-intensive tasks and help improve productivity. Thus, cost saving is definitely a core opportunity for companies setting expectations and measuring results for AI initiatives. While many financial services companies agree that AI could be critical for building a successful competitive advantage, the difference in the number of respondents in the three clusters that acknowledged the critical strategic importance of AI is quite telling (figure 3).

Artificial Intelligence and the Insurance Industry

While AI is transforming the industry, it is also raising critical questions about the relationship between machine learning and automated decision making. As AI is increasingly deployed in various areas, notable legal and regulatory challenges arise, including managing third-party risks. AI has moved centre stage as a boardroom issue, demanding C-suite attention to navigate the opportunities for integrating this novel and exciting technology while addressing legal and ethical concerns. Internally, the AI-first institution will be optimized for operational efficiency through extreme automation of manual tasks (a “zero-ops” mindset) and the replacement or augmentation of human decisions by advanced diagnostic engines in diverse areas of bank operations. These gains in operational performance will flow from broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real time. “Our Data Center platform is powered by increasingly diverse drivers — demand for data processing, training and inference from large cloud-service providers and GPU-specialized ones, as well as from enterprise software and consumer internet companies.

Layer 2: Building the AI-powered decision-making layer

Additionally, the platform analyzes the identity of existing customers through biometric authentication and monitoring transactions. Additionally, 41 percent said they wanted more personalized banking experiences and information. AlphaSense is valuable to a variety of financial professionals, organizations and companies — and is especially helpful for brokers. The search engine provides brokers and traders with access to SEC and global filings, earning call transcripts, press releases and information on both private and public companies. Undoubtedly, AI’s advancements are reshaping customer experiences and industry landscapes at an unprecedented pace. Our company’s CEO and CTO, Mark J Barrenechea, put it best when he was describing this swift evolution, remarking in an interview for CIO Views, “We have never moved so fast, yet we will never move this slowly again.”

AI in Personal Finance

A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. One of the most significant business  cases for AI in finance is its ability to prevent fraud and cyberattacks. Consumers look for banks and other financial services that provide secure accounts, especially with online payment fraud losses expected to jump to $48 billion per year by 2023, according to Insider Intelligence.

AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets. Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions. Here are a few examples of companies using AI to learn from customers and create a better banking experience. Let’s take a look at the areas where artificial intelligence in finance is gaining momentum and highlight the companies that are leading the way. In addition, the advent of robo-advisors further catalyzed this shift by employing algorithms to create tailored investment profiles based on risk assessments and financial objectives. This innovation significantly slashed costs compared to traditional financial advisory services, making investment avenues accessible to a broader spectrum of individuals.

To boost the chances of adoption, companies should consider incorporating behavioral science techniques while developing AI tools. Companies could also identify opportunities to integrate AI into varied user life cycle activities. While working on such initiatives, it is important to also assign AI integration targets and collect user feedback proactively. We found that companies could be divided into three clusters based on the number of full AI implementations and the financial return achieved from them (figure 1). Each of these clusters represents respondents at different phases of their current AI journey.

For example, Public’s Alpha assistant can perform high-level stock research for clients by drawing upon the firm’s internal market data sources. Below is a screen shot of the Alpha assistant answering a very specific question around the performance of a particular stock in Q3 2022. Both firms provide heavily caveated, high-level advice only when given sufficient background on the user’s financial situation.

Click through the wheel to navigate the key themes of the publication.

A Vectra case study provides an overview of its work to help a prominent healthcare group prevent security attacks. Vectra’s platform identified behavior resembling an attacker probing the footprint for weaknesses and disabled the attack. Time is money in the finance world, but risk can be deadly if not given the proper attention. Having good credit makes it easier to access favorable financing options, land jobs and rent apartments.

The interaction between AI and data protection legislation is complex andstill not fully resolved with additional challenges being posed by GenAI. We also explore how otherkey jurisdictions are tackling the issue by considering the impact of new data protection regimes inAsia and the U.S., . AI presents unique ethical and practical dilemmas, and this material challenge is quickly gaining traction among governments and regulators who are increasingly collaborating in the hope of establishing the global standards needed to support the safe adoption of AI in all sectors. Across regions and sectors we have seen a range of regulatory approaches emerge, with AI garnering significant interest from financial regulators. Firms are also adapting generative AI to help fight financial crime, with a broad range of use cases — including the slow and expensive, but vital, field of anti-money laundering and ‘know your customer’ protocols. Among the data sets that their systems study are executives’ calls with analysts, in which they can scan for clarity of purpose, analyst responses, and whether companies’ results live up to what their bosses are saying.

Another major use case for fraud detection and prevention in banks is the use of data analytics. Banks can use data analytics to combine information from multiple sources, such as transaction data, customer data and external data sources, to create a more complete picture of a customer’s behavior. This can help banks identify suspicious activity that might not be apparent from any single data source. An in-house LLM lets your firm leverage proprietary data to create very unique generative AI services. The high cost and complexity means that this option will not be feasible for most financial services firms.

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. The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions. Just as banks could believe they were finally bridging the infamous divide between business and technology (for example, with agile, cloud, and product operating model changes), analytics and data rose to prominence and created a critical third node of coordination. While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent. Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities. In our experience, this transition is a work in progress for most banks, and operating models are still evolving.

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