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Generative AI vs Machine Learning: Key Differences and Use Cases

Generative AI Vs Machine Learning: Key Differences And Use Cases



applications of ai in banking :: Article Creator

The Future Of AI In Financial Services

The industry's AI spend is projected to rise from $35 billion in 2023 to $97 billion by 2027, which ... [+] represents a compound annual growth rate of 29%.

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The Financial services sector continues to make notable progress with gen AI. In just the past couple of months, we've seen the launch of several gen-AI powered solutions, including Morgan Stanley's tool that summarizes video meetings and generates follow-up emails, as well as JPMorganChase's AI assistant LLM Suite. Others, like BNP Paribas and TD Bank, have announced important partnerships with gen AI model builders.

The industry's AI spend is projected to rise from $35 billion in 2023 to $97 billion by 2027, which represents a compound annual growth rate of 29%. The largest players are aggressively investing in developing their AI infrastructure and scaling use cases to capture more value. Daniel Pinto, JPMC's President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value.

The question now is what will Financial Services do next and how soon will they apply AI across the entirety of their organizations and more broadly with customers.

Based on my conversations with senior leaders and through Accenture's work running the FinTech Innovation Lab, I predict we'll see gen AI evolve over two time horizons: one that's happening now — which will see the rapid adoption of AI assisted tools and the availability of technologies for handling unstructured data and data gathering — and one further into the future, with more sophisticated applications as the infrastructure, modeling and regulatory considerations advance.

The more immediate time horizon is seeing FS firms focus on four areas:

1. AI co-pilots – Co-pilots that work alongside employees will streamline workflows and provide new insights, leading to significant productivity improvements. Citizens Bank for example, expects to see up to 20% efficiency gains through gen AI as it automates activities like coding, customer service and fraud detection. In the future, these co-pilots could tailor investment strategies in real-time or predict market trends, helping to fortify FS firms' competitive edge and deliver differentiated client outcomes.

2. AIways-on AI web crawlers – These web crawlers continuously gather and analyze data from various web sources and public records. They can track real time financial news and market movements while detecting subtle changes in consumer sentiment on social media platforms, alerting banks to the potential risks and opportunities while enabling proactive management.

3. Automating unstructured data tasks - Gen AI systems will process and analyze unstructured data—emails, documents, and multimedia content—transforming it into structured, actionable insights and reducing the time traditionally required for data management. This shift will allow employees to concentrate on more high value tasks, like strategic decision-making and creative problem-solving. We've already seen financial services clients deploy a large number of highly-focused generative AI agents that can automate processes with little or no ongoing human input.

4. Hyper-personalization – Banks and others are leveraging AI and non-financial data to better create and target highly personalized offerings. This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive. Take Klarna's AI assistant as an example. It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong.

Further into the future – risk management and new propositions via synthetic data

As the technology and infrastructure advance to allow for more sophisticated models with larger data sets at a lower cost than today—and as regulatory policy takes shape—we expect to see financial services delve deeper into using gen AI to tackle risk management and to deliver a richer customer experience.

Gen AI could play a critical role in risk management through the creation and use of synthetic data, which will become essential in enhancing the accuracy of predictive models, especially for fraud detection, and help Financial Institutions proactively safeguard against threats and make more informed decisions. One European neobank, bunq, is already using generative AI to help improve the training speed of its automated transaction monitoring system that detects fraud and money laundering.

More broadly, gen AI could transform compliance and security measures, enabling firms to meet regulatory requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk.

Synthetic data could also lead to a better customer experience through the designing and testing of new propositions, such as loans or investments. Banks can use the data to simulate how customers might respond to these new products or to other scenarios, like a financial recession. Some FS firms are already trialing tools in this space, but it may take some time before they are truly enterprise ready.

Fintechs will help democratize gen AI

Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services. For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. Others include Reality Defender, whose deepfake detection platform helps banks, insurers and governments detect AI-generated content at scale, and Hyperplane, a data intelligence platform that lets financial institutions develop personalized experiences and predictive models through proprietary large language models (it was recently acquired by Nubank).

Many fintechs will play an enabling role by helping to democratize gen AI's capabilities for mid-market and smaller financial institutions, allowing these firms to leverage gen AI in a way that currently is only available to the largest FS players in the world.

Financial services have made considerable progress adopting gen AI in the last two years. While there's been a sizable focus on efficiency and cost optimization thus far, many FS CIOs are eager to deliver top line growth. To do so, they'll need to work closely with the business to consider how gen AI can lead to new ways of working, new products and new capabilities that can help accelerate revenues. The future of AI in financial services looks bright and it will be interesting to see where firms go next.


Enterprise AI, Banking Language Models And Open Finance Agents For Banking Growth

Dr. Ravi Gedela, CEO, Banking Labs Inc., an AI-powered financial intelligence company.

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Stepping out of a comfortable corner and looking around, bank executives evidenced that thousands of banks across the globe are experiencing stagnated growth. Recent innovations, specifically GenAI, stimulate a huge wave of excitement and expectations.

According to a 2023 McKinsey report, increased productivity through GenAI implementation in the banking industry could add between $200 billion and $340 billion in value annually. Is GenAI the silver bullet?

Researcher Carlota Perez wrote that techno-economic paradigm changes at scale seem to recur every 60 years, and each wave offers great new wealth-creating potential. It normally happens in a particular region of the world and unevenly spreads to the rest of the globe through a trajectory of boom and bust, which results in wealth redistribution. The real estate and financial markets seem to be impacted first and foremost by this GenAI competitive advantage.

The question is: How can banking embrace AI—specifically, GenAI? How will AI transform the risks and uncertainties into new growth opportunities?

In short, banks need to embrace AI-powered digital transformation strategically toward open finance—where banking will become much more open and transparent, and AI will be effectively governed and democratized to benefit customers and, hence, the community and society. For example, we've created our Digital Trinity solution as a new growth engine for the financial sector and in closely related industries such as real estate markets.

This can be done locally from inside each bank but in collaboration with financial regulatory agents and governing authorities. New monetary policies must be in place to tackle the financial instability and market uncertainty systematically. It is strongly recommended that each bank develop an operable roadmap following our STAGE framework considering strategy, risk, cost, control and talent.

In a nutshell, it is a three-stage journey. Each stage can be built up in parallel, but in planning, we found it to be more effective if it incrementally iterated three sets of transformative activities toward AI-powered banking. Each stage is a substantive undertaking.

1. Building up the enterprise AI foundation. This included new fine-tuning of the current banking business model toward AI-powered products and services.

2. Building up private or bank-specific LLM (or simply banking language model). The origin is our Banking Labs LLM.

3. Building up AI-powered and robotics-automated open finance agents. Open finance instead of decentralized finance is our proposed and preferred approach. We foresee the upcoming new global ecosystem for financial institutions, insurance and real estate markets.

The second stage is the most debatable. This is the question of whether banks should simply use the commonly available LLMs with techniques of prompt engineering, retrieval augment generation (RAG) or parameter-efficient fine-tuning (PEFT), or build their own proprietary LLM to support enterprise AI. Another promising alternative is to leverage increasing longer and larger context windows each major LLM offers, which is to use vectors of tokens to pivot the model with domain-specific data.

Ultimately, the dependency on external models can present challenges and push up the cost of using them. This is why developing proprietary LLMs remains a promising option. It is being debated in every corner of the GenAI space. The reasons are simple but have long tail consequences.

LLMs are compounding the effect of the expected potential to somewhat revolutionize natural language processing tasks in finance. This is not coincidental. Financial institutions accumulate high-quality data and directly engage customers impacted by multimodal interactions, on which LLMs thrive.

More advantageous is that financial institutions can translate investment into rapid returns in the forms of better customer experience, lower operational cost and new business growth. BloombergGPT presents a successful story, which uses 700 billion tokens to build its own foundation models—more than half of which are leveraging financial data.

However, developing proprietary models is not without challenges, which include high costs as well as a lack of talent and established processes. This also depends on the size and quality requirements of the targeting models. We recommend "responsible AI" with a "govern-to-empower" approach to address these challenges to financial institutions in embracing AI and LLM.

We advocate for adopting a data-centric approach to enterprise AI and banking LLM, with substantial consideration of the crucial role of data acquisition, cleaning and preprocessing. With the shifting paradigm of banking toward open finance, AI agents or a robotics workforce will play critical roles in complementing the human workforces with an optimal AI governance model and operational guidelines. With AI-powered new products, platforms and processes, banks can quickly profit from new growth opportunities and effectively mitigate risks.

In conclusion, we propose developing banking language models so that financial institutions can enable enterprise AI as a strategic approach and embrace GenAI for business growth. Our approach is based on three principles: democratization, a data-centric focus and standardized processes using a five-layer framework—which includes a data source layer, a data engineering layer, an LLM layer, an open API layer with gated authentication and authorization, and the application layer.

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AI Tools More Popular Behind The Scenes, Schwab Survey Shows

There's widespread recognition that advisors of all sizes need to begin adopting generative AI to keep growing, but how advisors apply AI tools is moving slowly and runs a gamut.

There are some advisors who are testing certain AI applications solely in back-office operations like automating meeting summaries, or managing client onboarding and portfolios. Others are using AI in more client-facing activities, like for marketing to prospects or existing client engagement through chatbots.

READ MORE: From writing assistance to presentation builders: Top AI tools picked by wealth leaders

A newly released 2024 Advisor Outlook Study by Schwab Advisor Services found that 62% of more than 1,000 independent investment advisors said they plan to use AI tools to automate routine tasks, next to 39% who said they'd use AI to enhance risk management and compliance efforts. The results contrast with another 21% who said they'd use AI to automate client services, like a chatbot, or for marketing (35%).

"The most easily adopted piece has been where advisors are finding something that's not directly in front of clients because some clients might feel a little nervous about some AI," said Jordan Hutchison, vice president of technology and operations at RFG Advisory. 

Hutchison said the AI notetakers, like Jump and Zocks, which are built for the advisor space, have grown in rapid popularity with advisors as well as the number of tech developers providing such services. 

"It has been like wildfire," said Hutchison, whose firm is also testing AI notetaker apps. "It went from like one [provider] to now literally, every company's got one." 

Hutchison will be speaking on AI use cases in financial planning during Financial Planning's first conference dedicated to AI, ADVISE AI, October 9-10. 

However, there's still greater hesitancy with advisors using AI tools for marketing or client-facing engagement. Part of the caution is regulatory. Officials at FINRA and the U.S. Securities and Exchange Commission have been increasingly vocal warning the industry about how AI is being marketed to investors through advertisements and public disclosures. 

READ MORE: From dictation to data leaks: FINRA, SEC scrutinize AI risks

There's also a hard-pressed desire by both advisors and clients to keep the human touch in the relationship, which is another reason why advisors tend to apply more back-office AI tools. 

"We view AI at Orion as assistive technology," said Adam Palmer, vice president of strategic product development at Orion Advisor Solutions, a leading wealthtech platform and AI developer for advisors. "We do not take the stance or view of AI as a replacement to the advisor-client relationship because you still need that human connection about something so vulnerable, being their finances. . . .The last thing you want is to be left without that human connection around that very vulnerable topic."

Still, there's strong industry hype about how AI can be used to attract clients. 

A new survey conducted by Financial Planning of 270 professionals in wealth management found that 43% said generative AI tools will play a "very important" or "extremely important" role in their firm's efforts to capture new clients and/or retain children inheriting wealth from existing clients. 

Yet, when asked how effective their firm has been in leveraging any technology the past few years to market to new clients, only 21% said it was "very effective" to "extremely effective." Another 46% said it was only "somewhat effective."

Part of this lackluster result might be due to slower adoption rates of AI. Most studies of advisors in recent months show about 30% to 40% of respondents have plans to adopt AI tech. Financial Planning's survey found 33% feel their firms currently view gen AI and its potential contribution as a high priority. 

"The 33% figure reflects early movers who are already exploring how AI can optimize back-office functions, enhance compliance and improve client service," said Ritik Malhotra, founder and CEO at Savvy Wealth, a leading wealthtech software provider. "It's fair, but as we progress, that number will rise as more firms recognize AI's transformative power in supporting advisors rather than substituting their expertise."

Likewise, the Schwab survey found that just 23% of RIAs have begun implementing AI tools in  some way at their firm, and 30% said they don't know their firm's plans for AI implementation. However, 54% of RIAs said they expect AI implementation to have the greatest impact on industry growth over the next three years. 

READ MORE: How to get advisors to use new tech: Test it on them first

"Generative AI is still at such an early stage, and RIAs are in test-and-learn mode. Some early

use cases suggest that as this technology matures, it has the potential to drive meaningful impact and improvements for RIAs of all sizes," Tom Bradley, chief client officer at Schwab Advisor Services, said in the report. 

READ MORE: RIAs need to be using AI tools 'yesterday,' tech leaders say

Schwab conducted the survey through June 28. The 1,088 respondents were independent investment advisors who custody assets with Schwab Advisor Services, representing a total of $451 billion in assets under management.

Despite a firmwide push to adopt AI, more than 80% of advisors agreed that more work needs to be done before AI's full benefits can be realized, according to the Schwab survey. 

"Once the primary fintech providers integrate generative AI into their platforms, mass adoption will follow," said Nyle Bayer, chief marketing officer at Future Proof. "Right now, the technology is still new, and adoption in finance is historically slow. We need larger fintechs to lead the way and set the standard for the rest of the industry."








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Generative AI vs Machine Learning: Key Differences and Use Cases

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