Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
I spent a decade consulting for financial services firms. Every AI implementation I saw followed the same pattern: pilot projects that looked impressive in presentations but stalled in production.
That’s changing. Banks are now deploying generative AI at scale, and the results are measurable. Here’s what’s actually working, based on implementations you can verify.
Specialized transformer models help finance units automate functions such as auditing andaccounts payable, including invoice capture and processing. With deep learning functions, GPT models specialized in accounting can achieve high rates of automation in most accounting tasks.
Generative AI models can produce more natural, contextually relevant responses because they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems and chatbots by providing more accurate, engaging, and nuanced interactions with users.
Conversational finance provides customers with:
For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by leveraging the company’s internal research and data as a knowledge resource.
For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry. To explore the many ways conversational AI can enhance customer service operations, look at our dedicated article on conversational AI for customer service.
AI plays a significant role in the banking sector, particularly in loan decision-making processes. It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk.
However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications.
A conditional generative adversarial network (GAN), a type of generative AI, was utilized to generate user-friendly denial explanations. By organizing denial reasons hierarchically from simple to complex, two-level conditioning is employed to generate more understandable explanations for applicants (Figure 3).
In a case study, the investor relations team anticipates a strong market reaction to the company’s quarterly financial results and needs to prepare a comprehensive script and potential investor questions for the earnings call.2
An analyst imports financial data from the current and previous quarters into a spreadsheet and uses a generative AI tool. The AI is given context from past earnings calls and specific insights to generate relevant commentary.
The AI tool generates a script for the earnings call, including likely investor questions and responses. The analyst formats this content into a Word document, highlights key investor questions, and prepares it for managerial review and the CFO’s preparation.
Banks still run software written in COBOL from the 1970s and 80s. Finding developers who know COBOL is nearly impossible, but this software handles critical transactions and can’t just be turned off.
Generative AI models can:
Goldman Sachs confirmed that generative AI is now central to its application development and enhancement efforts. One bank’s developers validate AI-generated code, catching errors before deployment, but the AI does the heavy lifting.
Technology costs make up ~10% of a typical bank’s expenses. Speeding up development and reducing maintenance costs directly improves profitability.3
Banks aim to avoid relying on outdated software and are continually investing in modernization efforts. Enterprise GenAI models can convert code from legacy software languages to modern ones, enabling developers to validate the new software and saving significant time.
Employees at Goldman Sachs confirm that generative AI is a strong aspect of application development and enhancement.4
Banks produce thousands of documents daily: investment summaries, loan applications, client reports, and regulatory submissions. These documents pull from templates, but customizing them takes time.
Generative AI now handles this:
Generative AI improves forecasting by learning from historical financial data to capture complex patterns and relationships. When properly fine-tuned for specific banks and economic contexts, these models make predictions about:
The key phrase: “properly fine-tuned.” Off-the-shelf models hallucinate and make confident predictions based on patterns that don’t exist. Banks that succeed with AI forecasting invest heavily in training models on their specific data and validating outputs against expert judgment.
By analyzing large volumes of data, generative AI can improve the accuracy of financial forecasts, including stock prices, interest rates, and economic indicators.
An Asian financial institution is running a PoC to provide prompt-to-report functionality to 2,000 analysts and users.5
Generative AI can automatically create well-structured, coherent, and informative financial reports based on available data. These reports may include:
This automation streamlines the reporting process, reducing manual effort and ensuring consistency, accuracy, and timely report delivery.
AI can simulate different regulatory scenarios and generate reports to help financial institutions ensure compliance with all necessary requirements under various conditions.
Learn AI text generation use cases and real-life examples.
Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data.
The enhanced understanding of fraud patterns enables these models to identify suspicious activity more accurately and effectively, leading to faster fraud detection and prevention. By incorporating generative AI in fraud detection systems, financial institutions can:
Explore how generative AI legal applications can help take actions against fraudulent activities.
Mastercard needed a faster, more accurate way to detect fraudulent transactions as fraudsters exploited stolen payment card data. Using generative AI, Mastercard scanned transaction data across millions of merchants to predict and detect compromised cards, helping banks block them faster and prevent fraud.
Results:
As highly regulated industry players, banks get regular requests from regulators.
Banks are running PoCs to see if they can use LLMs to respond to simple and less critical queries from regulators. 6
Another financial application of generative AI can be portfolio optimization. By analyzing historical financial data and generating various investment scenarios, generative AI models can help asset managers and investors identify optimal asset and wealth management, taking into account factors such as:
These models can simulate different market conditions, economic environments, and events to better understand the potential impacts on portfolio performance. This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios. This ultimately leads to improved financial outcomes for their clients or institutions.
Generative AI can simulate extreme market conditions that have not occurred in the historical data, allowing financial institutions to better prepare for rare but high-impact events.
AI models can generate synthetic borrower profiles to test the robustness of credit risk models, improving the accuracy of credit scoring and default predictions.
Generative AI models can be trained to understand the normal patterns of transactions and generate data points that represent outliers or anomalies. This helps in identifying potentially fraudulent activities or unusual transaction patterns that might indicate money laundering.
Since real fraudulent transactions are rare, generative AI can create synthetic examples of fraudulent activity, helping to train better detection algorithms.
Customer financial data is proprietary and regulated under GDPR, CCPA, and other privacy laws. This creates problems:
Synthetic data enables:
The synthetic customers have realistic credit scores, transaction patterns, income levels, and financial behaviors but they’re not real people, so no privacy violations occur.
Since customer information is proprietary data for finance teams, it poses challenges for its use and regulation. Generative AI can be used by financial institutions to generate synthetic data that complies with privacy regulations such as GDPR and CCPA.
Morgan Stanley faced the challenge of optimizing wealth management operations and enhancing advisor-client interactions through advanced AI tools while maintaining data security and minimizing errors.
They partnered with OpenAI to implement a generative AI platform for synthesizing research data. They piloted the tool with 900 advisors and planned a broader rollout.
The AI tool enhanced advisors’ ability to efficiently process large volumes of data. Morgan Stanley is scaling the platform while addressing risks such as AI errors and data security issues.7
These models can simulate various market scenarios, helping traders and portfolio managers understand potential risks and returns under different conditions.
According to Dimension Market Research, the size of the global market for generative AI in trading is projected to be USD 208.3 million by 2024 and USD 1,705.1 million by 2033. In 2024, the market is expected to grow at a compound annual growth rate (CAGR) of 26.3%.8
Generative AI can analyze individual investor profiles, preferences, and financial goals to generate personalized investment portfolios. This is particularly useful for robo-advisors and wealth management platforms.
AI can create personalized insurance products based on individual risk profiles, generating unique terms and pricing structures for different customers.
Generative AI can help insurers and lenders develop dynamic pricing models that adjust in real-time based on new data, market conditions, and individual customer behavior.
AI can generate different risk scenarios, helping underwriters assess potential outcomes and set appropriate premiums or interest rates.
By leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users.
These models can be trained on large datasets of financial knowledge to respond to a wide range of financial queries with appropriate information, including topics like:
For example, BloombergGPT can accurately respond to some finance related questions compared to other generative models.
Learn how to use ChatGPT for your business.
Sentiment analysis, an approach within NLP, categorizes texts, images, or videos into negative, positive, or neutral emotional tones. By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings.
Financial institutions can benefit from sentiment analysis to measure their brand reputation and customer satisfaction through social media posts, news articles, contact centre interactions or other sources.
For example, Bloomberg announced its finance fine-tuned generative model, BloombergGPT, which is capable of making sentiment analysis, news classification, and some other financial tasks, successfully passing the benchmarks.
Check out our article on stock market sentiment analysis to learn more.
Here are some reasons why some financial professionals hesitate adopting generative AI tools in finance:
Explore 10 major LLM risks and their impact.
Financial simulations and forecasts produced with the aid of enterprise generative AI are beneficial for trading, portfolio management, and financial markets. Despite its many advantages, including time savings, large data sets, and computational power, it can malfunction and expose sensitive data, posing security risks. These challenges can specifically affect finance processes and the overall finance function.
For additional insights into automation in the financial sector, explore our article on Intelligent Automation in Banking & Financial Services.
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