Transforming Contract Management In Banking And Enterprises With GenAI

In financial services, LLMs can analyze regulatory documents, generate compliance reports, and provide real-time responses to customer inquiries, enhancing efficiency and accuracy. Today, more than 50% of tech leaders within the financial services industry are interested in exploring AI applications, signaling a trend of increased adoption of this technology. Although many use cases may focus on customer experience applications, operational improvement is also an area of high value. In this environment, bank risk and compliance professionals have a unique opportunity to incorporate meaningful, measured GenAI capabilities into their workflows to help them manage risk, maintain compliance, and safely grow their business. Contract management is an area that poses a significant challenge in banking, financial institutions and enterprise operations.

More importantly, they can also open new revenue streams and create entirely new value propositions. Since ChatGPT 3.0 debuted in early 2023, generative artificial intelligence has been making waves in all industries, including financial services. AI co-pilots – Co-pilots that work alongside gen ai in banking 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.

By analyzing vast amounts of transaction data, AI models can identify unusual patterns that might indicate fraudulent activities. This proactive approach enables banks to mitigate risks more effectively, safeguarding customer assets. While using AI applications, data privacy and compliance with regulatory requirements are crucial for maintaining customer trust and meeting industry standards. AI-driven risk management solutions leverage LLMs to analyze vast amounts of transaction data, identify patterns indicative of fraudulent activities, and generate real-time alerts for potential compliance violations.

Ventures Exits M2P Fintech With 12X Returns

CaixaBank is exploring the use of explainable AI to enhance fraud detection systems while avoiding unwanted bias and discrimination. DeepBrain AI, a pioneering generative AI company, announced the launch of the latest iteration of its AI Bank Teller complete with next-generation machine learning. Developed in collaboration with Shinhan Bank, this deep learning technology aims to revolutionize banking by creating interactive AI versions of employees to service customers. AI-powered contract management solutions help comply with regulatory standards and mitigate risks effectively. They facilitate in monitoring modifications, changes and amendments in laws and regulations across different jurisdictions and country-specific regulations.

They also plan to expand its use within the next year to audience targeting (64%) and trend analysis (64%). In addition, more than three-quarters reported improvements in customer satisfaction and retention (82%); efficiency in processing large data sets (78%); and sales or market share from data-driven insights (76%). “But better anti-fraud safeguards are just one of many potential advantages awaiting firms that take the GenAI leap. In fact, leaders on the first wave of GenAI implementation are seeing early ChatGPT App returns on their investments in many areas of the bank.” The bank’s manual loan processing procedures were time consuming, complex and required the integration of data from multiple sources. Incomplete or incorrect information provided by the bank’s customers resulted in further delays in assessing their credit worthiness and approval of their loans. Banks, many of which are still heavily dependent on manual processes, continue to figure out ways in which they can effectively employ AI, Livingston said.

WorldClass: Empowering 100 million people

Timely and reliable data will drive the transformation – the more you trust the data, the faster you can move forward without fear of bias, hallucinations and other risks. This 2024 IBM IBV CEO Study revealed that product and service innovation is CEOs’ top priority for the next 3 years, with generative AI opening the door to a new universe of opportunity. Asteria plans to help its SME clients improve profitability, increase financial stability, and enhance financial acumen through broader implementation of its virtual advisor. In the report, the company focused on three types of threats – social engineering, malicious content delivery and Gen AI data security – as well as the top adversary groups. The digital nature of fintechs makes them an attractive target for cybercriminals seeking financial gain or attempting to disrupt the financial system. When ChatGPT launched in late November 2022, it took just five days to attract 1 million users.

Some financial institutions are pressing ahead and applying Gen AI tools to assessing and adapting both risk control frameworks and processes, as well as client onboarding and service journeys. They are beginning to see early gains in operational cost reductions, significantly improved client onboarding and servicing journeys, as well as dynamic financial crime controls. CFOs at financial institutions also worry about the nontrivial costs of resources required to operate the better-known generalized LLM platforms. Banks are increasingly turning to smaller, more specialized domain models that can be finely tuned on proprietary data, creating a competitive edge while also being more cost-effective. These domain-specific models require fewer tokens to perform tasks, thus reducing operational costs. Additionally, most established financial institutions as well as FinTech institutions rapidly progress from initial exploration with a single LLM to a portfolio of domain-specific models tuned for specific use case categories.

You can foun additiona information about ai customer service and artificial intelligence and NLP. The best banks of the future are going to leverage GenAI to leapfrog ahead when it comes to customer experience, engagement and outcomes. This technology has the potential to create simpler customer interactions, reducing the reliance on a touchscreen interface that we’ve had for the last 15 years. And we’ll do that by leveraging the linguistic power of ChatGPT to improve the understanding and quality of every interaction. Up until now, the use and understanding of language has been a uniquely human phenomenon. It’s what we use to connect with people, build relationships and engage with others.

Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness. Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction. By harnessing the power of AI-driven automation, banks and enterprises can streamline contract processes, optimize resource utilization and confidently navigate the complicated legal landscape. The paper takes a business-technical approach to highlight the deep potential of Gen AI  systems, the key quality determinants of such systems including its infrastructure to  contribute to the financial industry awareness and advancement.

And, much like the aforementioned JPMorgan, the bigger a financial institution is, the broader the service is to be hit by an attack, making cybersecurity considerations all the more important. As Sasso notes, the scope for Gen AI to automate a bank’s cybersecurity activities is vast. Yuen noted that HKMA received over 1,200 complaints related to cyber fraud in 2023, double the number in 2022. He is worried that losses could become big enough to threaten a bank, and even the banking system. Banks and commercial interests must collaborate more tightly with regulators and law enforcement to counter these threats, he said.

Being vigilant about fraud detection and prevention, while also maintaining strict safeguards on sensitive customer data, was a critical part of the bank’s loan processing capabilities and process. Given this complexity, it had become clear that a robust solution was needed in order to improve the efficiency of the bank’s loan review and approval processes and reduce the number of potential errors. “Gen AI represents a sophisticated human-computer interface that democratises  technology for daily users. Such technology-driven growth can flourish when there is  room for practical application – with balanced oversight – to enable the industry to  advance without stifling the needed breakthroughs.

Five Ways GenAI is Fundamentally Reshaping Banking and Capital Markets

We are also developing an enterprise knowledge base that will give our employees the ability to search and synthesise unstructured information for various tasks. Through GenAI code and tests generation, we expect significant time savings in software development. Artificial intelligence (AI) and generative AI (GenAI) are game changers, and we will see significant developments in the next five years that will fundamentally shape the way we work. The use of AI is not new to DBS, and we have been working with AI for over a decade now. “Getting people’s hands on the tools is sparking a lot of ideas … and once you see your colleagues doing it, it sparks some curiosity.” 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%.

Financial institutions must stay informed about evolving regulatory requirements and adapt their AI strategies accordingly. But gen AI represents a paradigm shift that can help banks more fully unlock the power of their data. Gen AI will elevate decision-making from an isolated exercise of pattern matching to a systemic view of how to optimise bank operations and customer relationships. If they don’t continually invest in new tools, they risk lagging their peers or falling prey to upstarts that can deliver cheaper, easier-to-use financial products. That happens with every industry, of course, but for banking fees, it’s a race to the bottom. As everyone becomes more efficient, the same services are always available for less somewhere else.

The Future Of AI In Financial Services

In contrast, LLMs are pre-trained on extensive datasets, allowing them to generalize across various tasks without extensive customization. This generalization capability reduces the need for domain-specific adjustments and enables LLMs to adapt to new use cases quickly. In financial services, this adaptability allows LLMs to handle diverse tasks such as compliance monitoring, customer service, and risk assessment with minimal reconfiguration. It’s in this context that we contemplate the next major technological wave – artificial intelligence (AI) – and its impact on the banking industry. AI has of course had a prominent role in banking and financial services for many years.

Half (51%) of banks said they prefer partnerships as their go-to-market approach for GenAI use cases, as opposed to in-house development. Rajeev Minocha, head of banking and capital markets at EXL, a leading data analytics and digital operations and solutions company. It’s clear that there are nagging concerns holding some banks and lenders back.

Quicker adoption, the ability to add in new functions and products at relative ease, as well as high availability infrastructure are what the banks need the most. According to Hovhannessian, a wider adoption of GenAI across banking institutions would not happen before it is integrated into a bank’s core system, a process that requires sophisticated coding, cautious scrutiny and absolute security. This is something Red Hat has been working on for almost ten years with Red Hat OpenShift, a hybrid cloud application platform enabling banks to modernise software on legacy systems as well as migrate to cloud-native systems.

However, the AI bank tellers perform more tasks than an ATM while maintaining a human touch. Banks are bullish on AI – 100% of banks surveyed are at least considering the adoption of artificial intelligence, with a significant 62% actively or aggressively exploring this transformative technology. Imagine a scenario where banks and large enterprises deal with thousands of contractual documents while smaller enterprises deal with hundreds. You may agree that each contract demands meticulous attention to detail, from initial drafting to negotiation, execution and ongoing management.

Its new solutions can be applied in customer and middle office operations or product development, enabling banks to create products in real-time based on customers’ preferences. The second factor which I believe deserves more attention is the potential of generative AI not only to increase productivity but also, and more importantly, drive revenue. Lately, a number of commentators have questioned whether businesses can actually do this or if the technology will end up being only an efficiency play. Most of the industry refutes this – Accenture’s Pulse of Change survey, published in June, found that 76% of banking executives globally view generative AI as more beneficial to revenue growth than to cost reduction. The survey gave us profound insights into prevailing market trends, and our experts will be on hand to present an in-depth exploration of the data and results at Sibos 2024, the premier annual event for the financial services industry.

Financial institutions must invest in research and development to enhance the interpretability of LLMs, ensuring that their decisions are transparent and accountable. Predictability requires rigorous testing and validation of AI models to ensure consistent and reliable outputs. By maintaining transparency and predictability, financial institutions can build trust with regulators, customers, and other stakeholders, demonstrating their commitment to ethical AI practices. What also marks out the banks out in front are their GenAI-focused talent and skills. People not systems drive innovation and make the most of the tech potential. Do neobanks and fintechs have a head start in capitalising on this potential?

Banking and Capital Markets

Instead, AI should handle data analysis and initial assessments, leaving the ultimate decision to human financial professionals. This approach ensures that AI serves as a powerful tool to enhance banking operations without overstepping its limitations. Banks are increasingly adopting generative AI to elevate customer service, streamline workflows and improve operational efficiency. This adoption advances the ongoing digital transformation of the banking industry. AI solutions simulate natural language by using natural language processing (NLP).

• RAG implementations involve combining LLMs with external data sources to enhance their knowledge and decision-making capabilities.
• They teamed with IBM Client Engineering to build Asteria Smart Finance Advisor, a new virtual assistant based on IBM watsonx Assistant, IBM Watson® Discovery and IBM® watsonx.ai™ AI studio.
• The paper suggests that financial institutions should implement specific controls for AI systems, including monitoring protocols and human oversight.
• The combined partnership of Oracle and NVIDIA accelerates ‌enterprise-wide adoption of AI.

It also simplifies risk management and regulatory compliance, providing a unified strategy for legal and security challenges. Generative AI-driven tools can also evaluate historical data, market trends and financial indicators in real time. This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations. These AI capabilities help banks optimize their financial strategies and protect themselves and their clients.

Larger banks further along in their AI experimentation should establish a control tower function to not only provide direction and vision, but also document a high-level roadmap to achieving the firm’s GenAI goals. Such a roadmap requires a rethink of the value chain and business model, a full assessment of technology architectures and data sets and evaluation of innovation investments. A control tower approach both ChatGPT provides GenAI leadership and coordinates ongoing execution and deployments. It’s critical that the right controls and metrics be put in place, with adjustments being made over time as business outcomes are tracked and needs change. All sizes of financial institutions can benefit by standing up a GenAI center of excellence (CoE) to implement early use cases, share knowledge and best practices and develop skills.

EY analysis suggests that rethinking the traditional financial institution with Gen AI at its core has the potential to create US$200b to US$400b in value by 2030. Additionally, productivity gains could reach up to 30% by 2028, supplementing new revenue opportunities. Many of the enabling technologies required for adaptive AI powered banking already exist.

Financial services CEOs in the region have acknowledged the necessity to evolve their business models to ensure sustainable outcomes for stakeholders and society, especially in the face of challenges, such as climate change and the rise of GenAI. Given the newness of GenAI and the limited tech capabilities of many banks, acquisitions or partnerships may be necessary to access the necessary skills and resources. GenAI’s ability to work with unstructured data makes it easier to connect and share data with third parties via ecosystems.

Generative AI models have been trained in a subset of all of the publicly available data that’s out there in the world – the good and the bad. We must work with these models to harness all the inherent strength they bring to the way a conversation is understood – and then augment that with our own data. Since Cora launched in 2017 it has handled over 55 million customer interactions. In 2024, Cora is expected to have 12–14 million conversations with customers – which will be similar to the level of customer interactions in our telephony centres and branches.