As an industry that is built on and thrives on data, financial services have always been at the forefront of adopting technologies that can create value from data. Financial services companies in many ways already have a head start in deploying AI and setting policies, framework and standards to stay in line with the regulatory scrutiny. While the industry seems to be bullish on the use cases of Generative AI in financial services, several data risks and challenges loom large in the minds of stakeholders and key decision makers.
Generative AI – Value proposition for Financial Services
Banks and other financial institutions have long been leveraging AI, but Gen AI has the potential to revolutionize a much broader array of business functions in the sector. Generative AI is highly capable in parsing and deciphering large volumes of data, making it highly lucrative. The technology is powerful enough to reshape the future of BFSI including financial advice, insurance claims processing, marketing, customer engagement and servicing. The primary value areas for the sector include-
Operational Efficiency
Financial services companies can significantly reduce operational costs and improve efficiency by automating routine tasks including information review, synthesis and comparisons. This allows resources to focus on strategic tasks and reduces errors in retrieving and interpreting data.
Customer experience
Customer experience is a key differentiator for most businesses today with customers increasingly favoring companies that give them unique and personalized experiences. Generative AI can be highly powerful in delivering effective customer experiences at scale. Businesses can use GenAI-powered chatbots and real-time assistants to simplify and expedite customer interactions, claims processing, financial advice and document processing.
Regulation compliance and Forecasting
The banking and financial services industry is characterized by rapidly changing regulatory and compliance requirements. Given the sensitive nature of data possessed by the BFSI sector, it is also paramount to maintain utmost governance and data security measures to prevent breach and fraudulent activities. Using Generative AI technology, it is easier to ensure faster synthesizing, summarizing and implementing new regulations. Gen AI could also help with more accurate predictions on risk exposure, interest rates or even potential defaults.
Market intelligence and portfolio management
Gen AI has highly advanced algorithmic simulations and reliable risk modelling and forecasting capabilities. Generative AI can easily assimilate unstructured data from various sources and predict market sentiments and financial trends. Stakeholders and strategic decision-makers can quickly reassess and modify data using the insights garnered.
Resisting Roadblocks to Generative AI Adoption in Financial Services
Collating, processing and storing data that are fed into Generative AI models comes with its owns set of challenges. For financial services executives planning GenAI adoption in their digital transformation roadmap, it is important to be mindful of the key bottlenecks to overcome –
1. Large Volumes of Unstructured Data
A large portion of data available with Banks and other financial services institutions are in unstructured form, making it difficult to analyze, process and store. While most financial services companies have developed strong capabilities in using structured data, they struggle with leveraging unstructured data. This is largely due to the lack of capabilities and infrastructure to use and deploy sophisticated AI models. Also, the unstructured and vast nature of datasets poses several challenges in recording and analysis.
2. Regulatory and Compliance concerns
Banking and financial services companies are highly bound by regulatory and compliance guidelines. Well-planned governance prevents sensitive data exposure including personal information and intellectual property. It is vital to note that giving generative AI access to confidential organizational documents will be subject to several legal, regulatory and policy discussions. Building an adaptive model for Generative AI framework’s governance is crucial to ensure quality and security of data.
3.Limited Computing Resources
Adequate computing resources are required to train and deploy GenAI models. Organizations with limited resources and outdated infrastructure struggle in this regard. For the financial services industry that is heavily populated with large databases of structured and unstructured data, high processing power and storage space is vital. However, the resources required to enable these functions are often expensive to acquire. Hiring for AI related roles in financial services such as data scientist and data engineers that are well-versed with the technology creates further hurdles for companies in the sector.
Building a Data Strategy for Gen AI Adoption in Financial Services
While seamless adoption of Generative AI for financial services comes with its own set of challenges, planning for and building a data strategy is the first step to avoid potential pitfalls. Businesses must aim to build a sound data strategy by-
Focusing on Data Quality & Accessibility
Financial services companies must have a data quality strategy in place to leverage the full potential of Generative AI use cases for business. Also, by having a strategy in place to make high quality data more accessible enables financial leaders in faster and accurate decision-making. If organizations rely on legacy systems to store and manage data, this could prove to be even more challenging. Eliminating such data barriers while not compromising on the data quality and accessibility within the organization can help financial services companies capitalize on advanced technologies like generative AI.
Robust Data Infrastructure
Unifying large volumes of data across systems and clouds is essential for Gen AI, necessitating the need for a modern data platform. Organizations must focus on building robust data platforms that possess advanced computational power, storage capacity, and managed LLM infrastructure that is required for seamless Generative AI operations.
Security and Compliance
With the average cost of data breaches for financial services firms reaching almost $6 million in 2022, implementing robust security measures is critical. A comprehensive data strategy for Gen AI implementation in financial services companies must prioritize privacy measures, particularly concerning sensitive financial data. Compliance with industry regulations is imperative to avoid legal repercussions and maintain the trust of clients and stakeholders. Regular audits of data practices should be conducted to identify and rectify potential privacy breaches or non-compliance issues promptly.
Regular Assessment and Updates
Within the framework of Gen AI implementation, establishing governance controls becomes essential for guiding ethical decision-making. This involves promoting fairness and ensuring that the decisions made by Generative AI systems align with the organization’s values. Regular assessments and updates of ethical guidelines are necessary to address potential challenges and changes in regulatory requirements. Moreover, employee education on ethical considerations related to Gen AI is crucial, empowering them to make responsible decisions that reflect the organization’s principles.
Data Governance Policy
Financial organizations aiming to implement Gen AI must establish a unified, governed, and secure environment. Strong governance controls are crucial to mitigating risks associated with privacy breaches, non-compliance with regulations, and reputational damage. These controls also play a pivotal role in ensuring that decisions made by Gen AI systems adhere to ethical standards, aligning with the organization’s values and regulatory requirements.
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