ChatGPT stands out as a highly disruptive technology in recent times, driven by the core innovation of Generative AI (GEN AI). Positioned as an advanced form of AI, the prowess and impact of GEN AI, as evidenced by technologies like ChatGPT, GitHub Copilot, and Bard, are substantial. Envisaging the broad adoption of GEN AI sparks anticipation for its transformative influence. In navigating the intricate landscape of complex data, Gen AI is poised to revolutionise the analytics paradigm by guiding businesses towards strategic foresights, enabling informed decisions at an unparalleled speed and accuracy across industries.
Understanding GEN AI
GEN AI Refers to a class of AI Models capable of generating new, often realistic, data content based on patterns learned during training. Unlike traditional AI Models, which are designed for predicting outcomes or specific tasks, GEN AI possesses the ability to generate new insights, and content, learn patterns and innovate in ways that were previously unimaginable.
Technically speaking, Gen AI leverages unsupervised learning techniques, allowing it to create novel outputs and adapt to dynamic datasets.
6 ways Generative AI could be used to improve productivity in banking
1. Credit Approval
Loan Applications: Generative AI-based chatbots can guide customers through the loan application process. Banks can also user generative AI to verify customer information
by conducting a natural language conversation. One of the primary methods is to analyse the customer’s credit history. This involves looking at factors such as the customer’s credit score, payment history, and outstanding debts. By analysing this data, banks can determine whether a customer is a high or low-risk borrower.
Another technique used by banks is to analyse the customer’s income and expenditure patterns. This involves looking at the customer’s income, expenses, and other financial transactions. By analysing this data, banks can determine whether a customer has a stable income, how much they spend on various expenses, and whether they have any outstanding debts. Cloud DataPlatform is being used to process so much data and train and test the model, creating the challenger model to find the accuracy of the model.
Credit Analysis: Credit analysts can use generative AI to assess creditworthiness by analysing customer credit scores and financial history. Additionally, It can measure the risk level of a loan application by examining data from various unstructured sources.
Propensity modeling is a statistical approach and series of techniques used by data scientists to estimate the likelihood of consumers to act on certain behaviors, like the propensity to buy a new financial product or service or to repay outstanding lines of credit debt. Essentially, different probabilities are assigned to consumers based on shared features (i.e. income range or age groups) to create accurate predictions of future behavior.
Combining disparate datasets with large amounts of transactional data, bank account/ product held data, and demographic data to create an accurate credit risk scoring model is difficult. Predictive analytics helps financial organisations better segment product holders and create scoring models that identify new opportunities, offering increased limits while minimising non-payment losses. Such scoring models can be built out of internal data and combined with external data sources, such as data that comes from risk score providers. Machine learning expands the model learning process, allowing more accuracy and consistency in results as new data is added to credit risk routines.
2. Loan Underwriting: 2020 set a new annual record for catastrophic events—defined as those with at least $1 billion in damages—with 22 such events in the United States in the insurance sector. In this constrained environment, improving underwriting performance is one proven way to boost competitiveness.
Even the leading insurers can see loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent, thanks to digitised underwriting. We anticipate that carriers will increasingly use the power of data and analytics to proactively assess their outlooks—similar to what hedge funds do in predicting capital markets—and identify market opportunities ahead of competition.
Leading carriers regularly tap once- unimaginable volumes of third-party data from diverse domains, including environmental data, industry-specific data, location data, government data, and more. They have built agile capabilities to obtain, test, maintain, use, and reuse the data in their models.
Also Read | Unlocking The Power of Gen AI: A Tech Odyssey in Insurance Innovation
Once borrowers are approved, loan underwriters can use generative AI to speed up the underwriting process. With generative AI, lenders can auto-generate sections of credit memos such the executive summary, business description, and sector analysis, and more.
External data serves as the catalyst unlocking the potential of artificial intelligence. It predominantly spans various industry domains such as financial services, agriculture, property rental, news feeds, and travel. Internet-derived data, encompassing pricing, semantic web, SEO, and web-related information, adds another layer. Environmental data, covering climate, sustainability, and weather, contributes to this diverse landscape. Public data, including government, open, training, and individual data like loyalty, demographic, and product reviews, further enriches the pool. Location data, derived from sources like cell towers, GPS, IoT sensors, satellites, traffic, and routing, forms an essential component.
Managing this array of data requires a robust data platform and cutting-edge technology for seamless integration, ingestion, and processing in a modernised manner. Cloud platforms, with their myriad features, resilience, and high Transaction Per Second (TPS) availability, emerge as a pivotal and highly sought-after solution in the present landscape.
3. Pitchbook Creation: Investment banking is a highly competitive, fast-paced industry in which banks need to get ahead to win deals. Pitchbooks are critical to winning business but they are extremely time-consuming. Junior bankers need to search through a multitude of unstructured internal and external sources, analyse data, and compile it into the correct formats. Generative AI can be used to gather, process, and summarise
information in seconds and create draft reports to be used in the final product.
4. Marketing and Lead Generation: Across industries, engagement models are changing: today’s customers want everything, everywhere, and all the time. While they still desire an even miX of traditional, remote, and self-service channels (including face-to-face, inside sales, and e-commerce), we see continued growth in customer preference for online ordering and reordering.
Winning companies—those increasing their market share by at least 10 percent annually— tend to utilise advanced sales technology; build hybrid sales teams and capabilities; tailor strategies for third party and company-owned marketplaces; achieve e-commerce excellence across the entire funnel; and deliver hyper- personalisation (unique messages for individual decision makers based on their needs, profile, behaviors, and interactions—both past and predictive).
At the top of the funnel, gen AI surpasses traditional AI-driven lead identification and targeting that uses web scraping and simple prioritisation. Gen AI’s advanced algorithms can leverage patterns in customer and market data to segment and target relevant audiences. With these capabilities, businesses can efficiently analyse
and identify high-quality leads, leading to more effective, tailored lead-activation campaigns.
Additionally, gen AI can optimise marketing strategies through A/B testing of various elements such as page layouts, ad copy, and SEO strategies, leveraging predictive analytics and data-driven recommendations to ensure maximum return on investment. These actions can continue through the customer journey, with gen AI automating lead-nurturing campaigns based on evolving customer patterns.
Within the sales motion, gen AI goes beyond initial sales-team engagement, providing
continuous critical support throughout the entire sales process, from proposal to deal closure.
With its ability to analyse customer behavior, preferences, and demographics, gen AI can generate personalised content and messaging. From the beginning, it can assist with hyper- personalised follow up emails at scale and contextual chatbot support. It can also act as a 24/7 virtual assistant for each team member, offering tailored recommendations, reminders, and feedback, resulting in higher engagement and conversion rates.
As the deal progresses, gen AI can provide real-time negotiation guidance and predictive insights based on comprehensive analysis of historical transaction data, customer behavior, and competitive pricing.
Also Read | Scope of Generative AI in Insurtech Space
There are many gen AI use cases after the customer signs on the dotted line, including onboarding and retention. When a new customer joins, gen AI can provide a warm welcome with personalised training content, highlighting relevant best practices. Chatbot functionality can provide immediate answers to customer questions and enhance training materials for future customers.
Gen AI can also offer sales leadership with real-time next-step recommendations and continuous churn modeling based on usage trends and customer behavior. Additionally, dynamic customer-journey mapping can be utilised to identify critical touch points and drive customer engagement.
5. Customer Service: Generative AI can assist with payment reminders, billing inquiries, and account management. It can also personalise recommendations for loan repayment based on a borrower’s financial history.
6. Debt Collection: Generative AI can also aid in debt collection efforts. It can interact with borrowers to provide repayment options, identify patterns of delinquency, and recommend appropriate collection strategies.
Views expressed by Dillip Kumar, SVP- Head of Data, Cloud Platform, HDFC Bank
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