Hyper-personalized Finance: A
Game Changer

Posted on : September 23rd 2024

Top Five Use Cases for Data Analytics in Banking & Finance

The banking and finance sector has witnessed a surge in personalization, driven by the integration of cloud computing, machine learning (ML), and advanced artificial Intelligence (AI).

End-to-end digital financial services, powered by AI-driven data, continue to gain relevance as customers increasingly expect convenience, agility, and personalized offerings from leading financial institutions.

Leveraging high-quality data analytics allows banks to innovate at scale, foster digital-first strategies, and strengthen customer relationships.

The diminishing costs for data storage and increasing computational power have accelerated automation and value creation across the industry. Institutions are now tasked with synthesizing real-time data from different sources, necessitating strategic decisions, including AI adoption.

AI’s use has exploded in banking and finance. Industry estimates suggest that 70% of a typical bank employee’s time is spent on operational activities, with only 30% devoted to customer interactions, revealing significant opportunities for AI deployment.

To stay competitive, banks and financial institutions must leverage AI-led data analytics to deliver personalized recommendations and targeted offerings, effectively rendering traditional approaches obsolete.

The following use cases exemplify the transformative potential of data analytics in banking and finance:

Use Case 1: Granular Customer Insights

Advanced data analytics enables banks to gain granular customer insights by analyzing vast datasets, including demographics and past financial behaviors. AI/ML technologies can process huge volumes of data, uncovering patterns and generating real-time insights.

A comprehensive 360-degree view of consumer behavior allows financial institutions to better understand their clients’ actual needs, rather than relying on conventional assumptions, making it easier to design personalized products and services.

Use Case 2: Redefining Credit Risk Modeling

Gen AI applications have the potential to revolutionize the entire credit life cycle. Large language models (LLMs) can process complex unstructured data, synthesizing insights into natural language summaries, such as reports and policy analyses. These tools can efficiently flag policy violations or errors during the credit underwriting process, streamlining information gathering, credit analyses, and documentation.

AI-powered systems can autonomously extract data, calculate key financial ratios, and summarize findings in credit memos, all with minimal programming or modeling expertise required.
AI-based analytics can also seamlessly monitor alternative datasets, providing credit scores in standardized formats. By offering natural-language-based explanations for credit scores, these systems enhance decision-making by clearly outlining the underlying risk factors.

Use Case 3: Smart Recommendation Engines

The rise of partnerships between large banks and financial institutions has spurred the development of AI-driven recommendation engines. These engines analyze data to personalize offers, such as reactivating long-dormant accounts through tailored promotions.

The integration of AI allows financial institutions to consolidate all types of analytics on a single platform, leveraging real-time insights to curate customized financial offerings that align with individual customer journeys.

Use Case 4: Extracting Critical Data for Market Research

High quality data is essential for informed decision-making. AI-driven web scraping techniques have made it easier to discover and analyze data at scale.

Web scraping techniques enable enterprises to gather a large amount of user-generated data from sources such as social media, mobile apps, websites, and online forums. This data can be used to enhance business processes and train custom AI models, providing valuable insights for market research.

Exhibit 1: Straive’s Data Science Methodology

Source: Straive Research

For example, web scraping can be employed to aggregate news articles, extract financial statements, and analyze insurance risks, contributing to real-time analytics and informed business strategies.

Use Case 5: Risk Management and Resilience Building

Dynamic risk models powered by advanced data analytics provides a deeper understanding of external changes enabling banks to better manage credit, fraud, and liquidity risks.

For investment banks, portfolio risk analysis is particularly critical, while insurance companies prioritize claims risk management.

One emerging application of predictive analytics is in environmental, social, and corporate governance (ESG) investing.

As greenwashing, the deliberate practice of making a company appear more sustainable (or green) than it is, becomes a growing concern, investors are turning to alternative datasets to verify ESG credentials.

AI-driven analytics can process real-time from various sources, such as social media, news articles, satellite images, and reports from independent organizations, helping to construct a more accurate and reliable ESG profile.

The Data Analytics Revolution Is Here

Banks and financial institutions can no longer afford to lag in adopting cutting-edge data analytics solutions.

AI systems have the potential to eliminate expensive manual processes and connect disparate systems, overcoming the challenge of siloed data.

A digital ecosystem that leverages data and AI for interconnected and on-demand services delivers significant value to customers and other stakeholders.

Falling behind on data analytics and AI implementation risks obsolescence, while embracing these technologies offers a path to competitive differentiation and sustained success.