Back-Office Operations, Risk Management, & Customer-Facing Frontiers – Is BFSI Ready for Generative AI?

Generative AI solutions is on the verge of transforming how we live, work, handle finances, and invest. So, we’ve reached a turning point where cloud-based AI outperforms humans in specialized skills.

The cool thing?

Its impact could be as game-changing as the internet or the advent of mobile devices. In fact, a whopping 82% of organizations either using or considering generative AI believe it will significantly change or transform their industry (source: Google Cloud Gen AI Benchmarking Study, July 2023).

What’s really shaking up the BFSI world is that any competitor can now harness and combine these AI tools for their benefit.

First off, gen AI brings a massive boost in productivity and operational efficiency. This is especially important in BFSI, where everything starts with contracts, terms of service, and agreements. Gen AI excels at sifting through and summarizing complex information, like mortgage-backed securities contracts or customer holdings across different asset classes.

But there’s more!

Foundational models like Large Language Models (LLMs) have an impressive grasp of human language and conversation context. These skills are a godsend for speeding up, automating, scaling, and enhancing customer service, marketing, sales, and compliance.

Gen AI isn’t just a tool; it’s like having a super assistant or coach for your employees. It helps them do their jobs more efficiently, freeing them up to focus on high-impact activities.

Front and Center in Finance: How Gen AI Reshapes Customer Interactions

Let’s delve into conversational finance – a specialized field where generative AI takes the spotlight. In this context, it revolves around AI-powered chatbots or virtual assistants that engage in human-like conversations using natural language processing (NLP), comprehension (NLU), and text generation (NLG).

Imagine this: generative AI models are transforming customer interactions by providing more natural and contextually relevant responses. They are trained to comprehend and mimic human language patterns, which, when applied to financial AI systems, significantly enhance the user experience.

Conversational finance is a game-changer for customers in several ways:

1. 1. Improved Customer Support: Customers receive more accurate, engaging, and detailed interactions.
2. 2. Personalized Financial Advice: Advice is tailored to each customer’s specific requirements.
3. 3. Payment Notifications: Customers stay informed about their financial transactions.

Additionally, for a broader overview of the use cases of customer service operations, you can visit our article on conversational AI for customer service.

Let’s shift our focus to another area where AI shines in the banking sector: loan decision-making. AI plays a vital role in this domain, assisting banks in evaluating creditworthiness, setting credit limits, and determining loan pricing based on risk assessment. However, transparency is crucial. Both decision-makers and loan applicants require clear explanations for AI-driven decisions, especially when loans are denied, to build trust and raise customer awareness for future applications.

Here, a conditional generative adversarial network (GAN), a type of generative AI, comes into play. It is designed to generate user-friendly explanations for loan denials. By categorizing denial reasons from simple to complex, this two-level conditioning system produces explanations that are easier for applicants to comprehend

Back Office Innovations in Finance with Generative AI

Improving Accounting Operations: Financial departments harness specialized transformer models to automate auditing and accounts payable tasks. Tailored GPT models equipped with deep learning capabilities are proficient in automating various accounting processes.

1. 1. Streamlined Document Analysis: Generative AI efficiently processes vast volumes of financial documents, extracting crucial information from reports, statements, and earnings calls, enhancing decision-making efficiency.
2. 2. Financial Analysis and Projections: Gen AI models, drawing insights from historical financial data, forecast future trends, asset prices, and economic indicators. Based on market conditions and variables, scenario simulations offer valuable insights into risks and opportunities.
3. 3. Automated Financial Reporting: Generative AI crafts structured, informative financial reports automatically, ensuring consistency, accuracy, and timely delivery. These customizable reports cater to specific user needs, adding significant value for businesses and professionals.
4. 4. Fraud Detection: Generative AI generates synthetic instances of fraudulent transactions to train machine learning algorithms, enhancing accuracy in identifying suspicious activities, bolstering security, and preserving consumer trust.
5. 5. Regulatory Requests: Banks are exploring the use of Large Language Models (LLMs) to handle simpler queries from regulators, displaying potential for efficiently responding to regulatory demands.
6. 6. Portfolio and Risk Management: Generative AI optimizes portfolio management by analyzing historical data to identify optimal investment strategies considering risk tolerance, expected returns, and market conditions, leading to well-informed decisions and improved financial outcomes.
7. 7. Synthetic Data Generation: Generative AI creates synthetic datasets adhering to privacy regulations, enabling financial institutions to use data for training models, conducting tests, and validation while safeguarding customer privacy.

For an in-depth exploration of synthetic data, refer to our articles comparing synthetic data and real data, or comparing synthetic data and data masking methods for data privacy.

Answering Your Financial Queries: How Generative AI Delivers Expertise

Generative AI, empowered by its expertise in understanding human language patterns and its ability to generate contextually relevant responses, takes center stage in offering precise and thorough solutions to your financial queries. These AI models can be fine-tuned using vast datasets of financial expertise, enabling them to handle a wide range of financial questions with pinpoint accuracy. They cover topics like accounting principles, financial ratios, stock analysis, and regulatory compliance. A prominent illustration of this capability is BloombergGPT, which excels in providing precise answers to financial inquiries, surpassing other generative models in the financial domain.

Source: “BloombergGPT: A Large Language Model for Finance”

Decoding Emotions: How Sentiment Analysis Elevates Finance

Sentiment analysis solutions, a component of Natural Language Processing (NLP), involves the task of categorizing texts, images, or videos based on their emotional tone, whether it is negative, positive, or neutral. This valuable tool enables companies to delve into the emotions and opinions expressed by their customers. With these insights in hand, businesses, including financial institutions, can shape strategies to enhance their services and products.

Financial institutions, in particular, can leverage sentiment analysis to:

1. 1. Assess Brand Reputation: By analyzing social media posts, news articles, contact center interactions, and various other sources, they can gauge the public’s perception of their brand.
2. 2. Evaluate Customer Satisfaction: This analysis extends to comprehending customer sentiment, aiding in the customization of services to meet customer expectations and boost satisfaction levels.

Gen AI: Redefining Value Creation for Businesses in Finance

Gen AI isn’t just another tech buzzword; it’s a game-changer for businesses. While it’s still in its early stages of deployment, the potential it holds for revolutionizing the financial services industry is immense.