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Generative AI in Banking: A Technological Revolution

According to a report by McKinsey, AI technologies could potentially deliver up to $1 trillion of additional value each year. This highlights the massive potential of Generative AI in revolutionizing the banking industry. It offers solutions to some of the industry’s key challenges such as enhancing customer service, bolstering security, making accurate risk assessments, and providing a personalized banking experience.

Generative AI, as the name suggests, is a form of AI that focuses on generating new instances of data that resemble the input data it was trained on. From creating realistic human faces to composing music, generative AI’s capabilities are truly vast. However, its potential is most palpable in sectors like banking, where constant innovation and adaptability are the keys to maintaining a competitive edge.

Gen AI is more than just ChatGPT, it has wide applications across industries.

Improving CX with AI-powered Customer Support Features

Generative AI is driving a paradigm shift in the way customer service is being delivered in the banking sector. Banks, including global leaders like Bank of America and Wells Fargo, have been using generative AI to develop advanced chatbots and virtual assistants. These AI-driven systems are trained on extensive datasets of customer interactions and are capable of generating personalized and accurate responses to customer queries.

Consider a customer asking, “What is the interest rate on a 30-year fixed mortgage?” The AI chatbot, with its ability to access the latest data from various lenders, can provide an accurate response. Furthermore, it can analyze the customer’s financial situation and provide personalized recommendations, such as potential eligibility for lower interest rates through refinancing.

The use of generative AI in customer service has two primary benefits:

  • Enhanced Customer Experience: With the AI system providing accurate and personalized responses, customers have a better and more satisfying experience.
  • Increased Operational Efficiency: AI handles routine queries, freeing customer service representatives to focus on more complex issues. This not only reduces the burden on human resources but also increases operational efficiency.

To highlight this, let’s take a look at a real-world example: Mantra Labs’ work with Viteos, a leading provider of investment solutions. Viteos’ financial asset management platform provides end-to-end middle and back-office administration for top-tier hedge funds, private equity, private debt, and other alternative asset managers. However, it faced several operational bottlenecks.

Mantra Labs, leveraging its expertise in UI/UX, ETL, and Machine Learning, refined the platform’s user workflows for more robust capabilities and smarter gains. An automated client onboarding solution was integrated, and a machine learning model was developed to analyze historical transactions, trades, and financial data from clients, accounting systems, and banks. This resulted in improved operational efficiency and a significant reduction in bottlenecks.

Using AI to Enhance Security

With the banking sector increasingly moving towards digital platforms, the importance of robust security measures cannot be overstated. Generative AI has emerged as a powerful tool to enhance security measures. Banks are using AI to detect and mitigate potential threats, providing an additional layer of security.

For instance, Capital One has been leveraging the power of generative AI to detect patterns indicative of fraudulent activity among the millions of transactions that occur daily. This real-time analysis and detection of potential fraud have been instrumental in enhancing the bank’s security measures.

Consider the workflow of this process:

  1. The AI system is trained on vast datasets of transactions, learning the intricate patterns of normal behavior.
  2. Once the system has been trained, it can generate new instances of normal behavior.
  3. Any transaction that deviates from these generated instances is flagged as potential fraud.
  4. This proactive approach to security has significantly reduced instances of fraud, thereby protecting the interests of the bank and its customers.

Refining Risk Assessment with Generative AIefining 

Risk assessment is a crucial aspect of banking operations. Traditionally, this has been a complex process involving the analysis of a customer’s financial history, current financial status, and market trends. However, generative AI has brought about a revolution in this area as well. By processing vast volumes of data, AI can make accurate predictions about the likelihood of a loan default. This helps banks make informed decisions and manage their risk more effectively.

Institutions like ING and the State Bank of India (SBI) have successfully integrated generative AI into their risk assessment processes. For instance, SBI’s AI system, aptly named “RiskEye,” analyzes a wealth of historical data and market trends to predict loan default risks. This valuable information aids in sound lending decisions, helping the bank avoid potential losses.

Personalizing the Banking Experience

Another transformative application of generative AI in banking is in the area of personalization. By analyzing a customer’s past transactions, preferences, and behavior, AI systems can generate personalized banking solutions.

Consider JPMorgan Chase’s use of generative AI. Their AI system uses customer data to create a personalized financial plan that suits the customer’s individual needs. This has not only improved customer satisfaction but also increased customer loyalty.

Challenges Still Remain

While generative AI offers immense potential, it also brings certain risks. These include:

  • Model hallucinations: This is when AI models produce authoritative-sounding answers to questions, even when they don’t have enough information to provide an accurate response.
  • “Black box” thinking: This refers to the difficulty in interpreting the output of the AI models or understanding how they produced it.
  • Biased training data: Like any AI solution, the quality of the source data is crucial. Any biases present in the training data can be reflected in the output.

Banks need to move swiftly to leverage AI opportunities, but they must also tread with caution to consider the legal, ethical, and reputational risks.

It’s clear that generative AI is not just another technology; it is setting new standards in banking operations worldwide. As we continue to advance in AI, its role in banking will only become more profound. It’s not just about the technology itself, but how it’s reshaping the entire banking landscape. As we move forward, the focus should be on constant innovation and adaptation to leverage the full potential of generative AI.

Want to read more on Generative AI?

Check our latest blog:

The Role of Generative AI in Healthcare

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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