AI in Financial Services: Key Trends & Opportunities for 2022

ai in financial services

Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized. An operating model is a representation of how a company runs, including its structure (roles and responsibilities, governance, and decision making), processes (performance management, systems, and technology), and people (skills, culture, and informal networks). A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture. An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution. To better drive the dissemination and use of AI solutions, Aleph Alpha founded a joint venture with PwC, a long-term strategic partner. Combining many years of consulting experience in compliance and legal services with Aleph Alpha’s innovative generative AI technologies, the partnership operates under the name

ai in financial services

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Gen AI can give developers context about the underlying regulatory or business change that will require them to change code by providing summarized answers with links to a specific location that contains the answer. It can assist in automating coding changes, with humans in the loop, helping to cross-check code against a code repository, and providing documentation. Watch this video to learn how you can extract and summarize valuable information from complex documents, such as 10-K forms, research papers, third-party news services, and financial reports — with the click of a button. Earlier in her career, she worked as a consultant advising technology firms on market entry and international expansion. Sameena has a PhD in Artificial Intelligence, an MS in Computer Science from IIT Delhi, and a BS in Electronics Engineering.

AI Companies Managing Financial Risk

ai in financial services

With over 20 years of expertise in the banking domain, they help top banks, NBFCs, and FinTechs create agile digital systems for regulatory compliance and comprehensive insights. DataRobot provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals. DataRobot helps financial institutions and businesses quickly build accurate predictive models that inform decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more.

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ai in financial services

Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. Financial institutions that successfully use gen AI have made a concerted push to come up with a fitting, tailored operating model that accounts for the new technology’s nuances and risks, rather than trying to incorporate gen AI into an existing operating model.

The Benefits And Risks Of AI In Financial Services

Making the right investments in this emerging tech could deliver strategic advantage and massive dividends.

  1. SESAMm’s TextReveal platform allows data scientists to extract and analyze their own insights from a proprietary data lake of more than 10 million new documents daily.
  2. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task.
  3. SESAMm is a leading artificial intelligence company serving investment firms and corporations globally.
  4. Keep up to speed on legal themes and developments through our curated collections of key content.

Operating-model archetypes for gen AI in banking

Additionally, Entera can discover market trends, match properties with an investor’s home and complete transactions. 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. Predictive modeling and a full 360-degree customer view were two components of a sales process update for the bank’s business and commercial banking clients. Optimized accounting equation definition cross- and up-sell leads and retention alerts have led to revenue growth of more than 8% of annual run-rate. For retail banks, a comprehensive set of AI and personalization capabilities—ready for deployment but easy to tailor—enable one-to-one marketing, so banks can better align offers with the needs and circumstances of each customer. In capital markets, gen AI tools can serve as research assistants for investment analysts.

AI’s prowess lies in its ability to automate mundane tasks and streamline processes. In the financial services industry, this efficiency surge has liberated advisors from routine duties, allowing them to focus more on strategic, advisory tasks. Gen AI, along with its boost to productivity, also presents new risks (see sidebar “A unique set of risks”). Risk management for gen AI remains in the early stages for financial institutions—we have seen little consistency in how most are approaching the issue. Sooner rather than later, however, banks will need to redesign their risk- and model-governance frameworks and develop new sets of controls. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues.

Once a customer is onboarded, insurance companies are using AI to receive and process insurance claims with high performance and accuracy. These processes are enabled by robotic process automation technology, which is a machine learning technique that enables hyper automation of various tasks. The dynamic landscape of gen AI in banking demands a strategic approach to operating models.

In the financial services industry, this technology can be applied to streamline deployment for workloads such as natural language processing (NLP), recommendation systems, and image recognition. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing. Making purposeful decisions with an explicit strategy (for example, about where value will really be created) is a hallmark of successful scale efforts.

Different models check which bank a statement is from, examine its veracity, and transform it into machine readable data which can be used to help make a decision. Financial institutions now hope that generative AI could replace these systems with alternatives that are more capable of responding to complex requests, learning how to deal with specific customer needs, and improving over time. Despite AI’s promise, it presents several potential drawbacks for financial services. Let’s look at what those are and what needs to be worked on to address these concerns. Undoubtedly, AI’s advancements are reshaping customer experiences and industry landscapes at an unprecedented pace.

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