The technical advancements in artificial intelligence (AI), machine learning (ML), deep learning (DL), and generative AI (GenAI) are moving fast. For most of 2023, GenAI dominated the news. A recent report found that 67% of companies have added GenAI into their corporate AI strategy, proving it’s not hype and here to stay. To be fair, business solutions that leverage AI are not new. What is new are the advancements that GenAI brings to the table, particularly in language creation, data handling, and content generation. In the financial sector, some companies are already beginning to embrace GenAI for content creation, operational efficiency, and risk assessment strategies to drive growth. 

Two GenAI Models to Know About

What makes GenAI so impactful? Broadly speaking, it can consume and analyze large datasets to create normalized human responses, making it valuable for many internal uses and customer journeys. GenAI’s power features will be transformational in the financial services industry. These features include:

  • Generation: generates answers, forms; and reports; submits documents
  • Classification: assigns labels, adds data to knowledge sets, provides real-time updates
  • Detection: performs accurate sentiment analysis and detects incomplete/fraudulent activity
  • Summarization: reviews vast datasets to create summary documents (FAQs, forms, etc.)
  • Extraction: automates data extraction and assigns data to relevant databases
There are many GenAI models available. Financial services institutions can achieve quick results with two of Google’s powerful models: text-bison and chat-bison. The text-bison model leverages natural language processing tasks such as sentiment analysis, entity extraction, and content creation. It can create document summaries, answer detailed questions, and apply labels to classify content. For a financial institution’s internal operations, it summarizes documents and helps underwriters find information like historical credit ratings and information required for loan applications. 

The chat-bison model, on the other hand, excels at understanding language, generating language, and handling conversations. Financial companies will find this model ideal for text-intensive tasks that require back-and-forth interactions, such as investment-related inquiries and application navigation assistance.

To support such models, the Google Cloud platform is leading the GenAI space in providing 360° features including infrastructure (VM with GPU/TPU), data services (Vertex AI) and AI services (GenAI Studio). Google’s GenAI solutions provide game-changing features like cloud-driven scalability, training pipelines, and more. Companies can customize the models, control default behavior, and evaluate output performance. Company data in model training is kept private and not used to train the broader model — a critical security and governance imperative, particularly in the context of sensitive financial data. 

Three Strategies to Drive Value  with GenAI 

Using the GenAI models above, financial services institutions can start driving value with content creation, new operational efficiencies, and improved risk assessment processing.  

  • Content Creation: GenAI’s mature processing power can rapidly and effortlessly produce content like reports, summaries, and forms, reducing the amount of time teams spend interpreting and generating textual information.  
  • Operational Efficiency: By automating manual data processing and accelerating preexisting automated data flows, GenAI addresses issues with data quality, and analyzes, sorts, classifies and assigns new data to datasets in real time.
  • Risk Assessment: Cloud services powered by machine learning (ML) already possess anomaly detection capabilities. GenAI adds accuracy and speed in identifying potentially fraudulent activity in real time. It can also analyze historical data to assess investment risks across time and market conditions. 

Recently, Wipro and Google’s GenAI models and the three aforementioned strategies to address a financial firm’s investor onboarding program. Wipro trained Google’s chat-bison model with the bank’s proprietary data (offers, accounts, loans, processes, etc.). The resulting chatbot responds accurately to open-ended customer questions such as “What is the process to open an account?” and “What are the fees?”. Integrating Google Doc AI  into the model is also helping the bank accelerate customer document verification by identifying missing or incorrect information on customer forms. Customers and prospects now have seamless and efficient customer journeys for onboarding, service, and account setup

Another bank wanted to improve its investor experience. Wipro trained Google’s text-bison to analyze investment options particularly related to investment risk. Using the chat-bison integration, financial advisors can provide real-time summaries of different investment instruments by sector or any scope of time. 

GenAI is a Game-Changer that Requires Responsibility

The accelerated advancements in GenAI have led to an explosion in computer-generated content. As a result, GenAI is also raising ethical and legal questions, and answering these questions is the critical final piece of any resilient GenAI strategy. 

While government regulatory guardrails continue to evolve, Google and Wipro align on addressing risks with the principles of Responsible AI. Any use of the technology should protect and enhance human dignity. It must safeguard privacy and champion the security of personal information. GenAI must not discriminate or undermine an individual’s identity, language, or background, and it should improve society and strengthen the values that bind us together. 

GenAI is a game-changer that will transform enterprise operations and enable new growth opportunities. To help companies benefit from GenAI, Wipro and Google expanded their partnership with Google Cloud and AI services. This partnership will help companies extend the benefits of GenAI across numerous enterprise functions, from customer experience and marketing to supply chain, financial modeling, and more.


About the Authors

Swapnil Zarekar
Senior Practice Manager, Wipro

Swapnil is a seasoned AI practitioner and consultant with over a decade of experience. He is senior practice manager with AI practice of Wipro Ltd and an expert in the Google Generative AI space. 

Dr. Magesh Kasthuri
Distinguished Member of Technical Staff, Wipro FullStride Cloud

Dr. Magesh Kasthuri is a Principal Architect at Wipro FullStride Cloud handling Pre-sales and technical consulting for BFSI customers. He is a multi-cloud Certified Architect and IBM Certified Bigdata Architect and a FinOps champion in Wipro.

Sachin Chandra
Global Practice Head, Wipro

Leading Wipro’s global practice team for Google Cloud. He is a hands on technology leader on cloud and Gen AI.