Torc blog

The BFSI Data Challenge: Beyond the Basics

The banking, financial services, and insurance (BFSI) sector is undergoing rapid transformation as AI-driven technologies redefine risk management, fraud detection, and customer experience. However, many financial institutions struggle to turn AI ambitions into operational success due to fragmented data infrastructures, regulatory challenges, and outdated legacy systems.

For AI to drive meaningful business outcomes, BFSI organizations must evolve beyond basic data governance and accessibility. This guide provides a structured, actionable roadmap designed for BFSI leaders looking to scale AI securely and efficiently.

👉 Want deeper insights? Download the full report for exclusive case studies and AI implementation frameworks to accelerate your AI journey.

📌 Torc has a community of over 1,000 AI and data professionals with deep BFSI expertise, providing access to top-tier talent that understands your industry's unique challenges.

The AI-Ready Data Playbook: Five Strategic Pillars

1. Assess Data Maturity: Where Are You Today?

Before building an AI-driven financial enterprise, organizations must first conduct a data maturity audit to identify gaps in quality, governance, and accessibility. A leading financial institution, for example, may assess:

• Data Lineage & Governance: How well do we track the origins, transformations, and uses of our data?
• Real-Time vs. Batch Processing: Can we support real-time AI-driven decision-making?
• Data Integration: Are our systems connected, or are we operating in silos?

Example: JPMorgan Chase implemented an enterprise-wide data fabric to unify risk and fraud analytics, enabling faster AI adoption while ensuring regulatory compliance.

Torc has worked with BFSI firms to implement AI-driven data quality checks, helping to improve data consistency and compliance readiness.

What’s Next?

Use data observability platforms like Monte Carlo or Collibra to identify and rectify quality issues before they impact AI-driven decision-making.

2. Break Down Data Silos: The Shift to AI-Driven Data Fabric

Data silos continue to hinder digital transformation, with 81% of financial institutions citing them as a major roadblock (Salesforce 2024 Connectivity Report). Rather than simply integrating disparate systems, leading BFSI organizations are adopting data fabric and data mesh architectures.

Key Considerations:

• Data Fabric: Ideal for organizations that need seamless AI data access across multiple cloud and on-premise environments.
• Data Mesh: Works well for decentralized teams where domain-driven ownership of data is essential.

Case Study: Goldman Sachs transitioned from a monolithic data warehouse to a distributed data mesh, allowing teams to access real-time analytics without sacrificing governance.

Torc has helped BFSI clients modernize their data infrastructures by implementing API-driven architectures, enabling smoother AI integration across departments.

📌 Learn how leading BFSI firms are breaking down data silos—download the full guide for more details.

What’s Next?

• Implement APIs and microservices to connect legacy systems with AI platforms.
• Use AI-driven metadata management tools to automatically classify and unify enterprise data.

3. Automate Data Governance: Compliance at Scale

BFSI leaders understand that AI is only as good as the data it is trained on. However, achieving regulatory compliance (GDPR, CCPA, PCI-DSS) at scale requires a shift from manual governance to AI-driven compliance automation.

Emerging Trends in AI-Driven Governance:

• Automated Data Classification: AI can tag and categorize sensitive financial data in real time.
• Self-Healing Data Pipelines: Platforms like Databricks and Snowflake are enabling predictive governance, reducing manual intervention.
• Federated Learning for Compliance: AI models trained on decentralized data help ensure compliance without exposing raw financial data.

Example: HSBC implemented AI-driven regulatory technology (RegTech) to automate compliance monitoring, reducing data processing errors by 40% (Boston Consulting Group AI Adoption Report).

Torc assists BFSI institutions in deploying AI-powered compliance monitoring tools to mitigate regulatory risks proactively.

4. Strengthen Data Security: AI vs. AI-Powered Cyber Threats

BFSI institutions are prime targets for cyberattacks, with the average cost of a data breach reaching $4.88 million in 2024 (IBM Data Breach Report). As AI adoption increases, so does the need for AI-driven security measures.

Key AI-Driven Security Enhancements:

• AI-Powered Threat Detection: Real-time identification of fraud and cyber threats.
• Zero-Trust Security Models: Continuous verification of access to prevent unauthorized breaches.
• Differential Privacy Techniques: AI can analyze data while ensuring customer anonymity.

Case Study: Citibank integrated AI-driven anomaly detection to monitor billions of transactions daily, reducing fraud losses by 30%.

What’s Next?

• Deploy homomorphic encryption to analyze encrypted data without decryption.
• Use synthetic data to train AI models while minimizing privacy risks.

5. Leverage AI as a Strategic Asset for Growth

AI is not just a compliance and security tool—it is a competitive advantage. Financial institutions must integrate AI into their core business strategies to drive revenue growth, improve customer experience, and optimize operations.

Key AI Use Cases in BFSI:

• Hyper-Personalized Banking: AI-driven insights help tailor financial products in real-time.
• AI-Enabled Risk Management: AI models predict and mitigate financial risks proactively.
• Automated Loan Processing: AI reduces processing times and improves decision accuracy.

Example: Wells Fargo leverages AI-powered predictive analytics for personalized banking, increasing customer retention by 15% (World Economic Forum AI in Financial Services Report).

What’s Next?

• Implement AI-driven customer engagement platforms for personalized financial services.
• Adopt automated credit risk models to enhance decision-making.

🚀 Ready to take action? Download now and future-proof your AI strategy.

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Artificial intelligence for IT operations is rapidly gaining ground. Almost a third of organizations plan to make significant investments in AIOps over the next 18 months.

Enticed by its ability to enhance system performance, minimize downtime and ensure operational compliance, the tightly regulated BFSI industry is now one of the biggest overall users of AIOps. As a consequence, BFSI organizations are facing increasing pressure to elevate their infrastructure, secure sensitive data and future-proof their systems to keep pace.

How can the financial sector capitalize on the AIOPs revolution to enhance their cloud practices?

Infrastructure monitoring and predictive maintenance.

Almost two in three IT organizations still rely on monitoring approaches that now lack the ability to cover the needs of their entire IT environment. They need a solution that spans their whole infrastructure: covering everything from on-premises hardware to the latest cloud systems.

AIOps is changing the game for organizations across industries, using advanced technology and new capabilities to enhance visibility and maintenance across their entire IT setup.

Advanced AI and ML algorithms

By using AI and ML to analyze large cloud datasets from in real-time, AIOps identifies patterns and anomalies that human operators might miss. These algorithms continue to learn and adapt from new data, improving accuracy and efficiency over time. This ability to adjust and pivot at speed allows AIOps to provide more precise insights and inform decision-making.

Using data from a range of sources

AIOps gathers data from servers, networks, applications and other sources, then unifies it into a single-pane-of-glass view to make analysis easier than ever. By collecting data from the entire infrastructure, BFSI organizations can establish both where things are working well and where the bottlenecks are, so they can keep things running smoothly. Ultimately, this minimizes operational risk, reduces maintenance costs and enables businesses to optimize their resource allocation for better ROI.

Identifying issues before they cause problems

It’s better to protect against risk than clean up after things go wrong. AIOps can detect early warning signs of cloud system failures, network congestion or software glitches by monitoring the infrastructure 24/7, enabling businesses to take pre-emptive action before issues snowball into major concerns. This avoids expensive downtime, prevents service blips, boosts customer satisfaction and safeguards the organization’s reputation.

Security and compliance.

The BFSI sector held more than 15 percent of the AIOps market share in 2022, and it’s only increasing its holding as time goes on. This is no surprise: due to the sensitive nature of financial and banking-related data, the industry has rapidly adopted the practice in order to keep their information safe.

With extra-vigilant threat detection and automated compliance reporting, AIOps isn’t just ahead of the game when it comes to cloud security—it works in line with regulations too, keeping financial organizations on the right side of industry standards and helping them avoid heavy fines.

Monitoring security incidents in real-time

With continuous monitoring capabilities, AIOps can keep constant watch over a company’s entire cloud infrastructure, detecting suspicious behavior or unauthorized access attempts immediately as they happen. This quick-time analysis lets financial businesses identify and respond to security threats quickly, reducing the risk of data breaches, financial fraud or cyberattacks and keeping sensitive customer information safe from prying eyes. What’s more, by analyzing security logs and network traffic patterns, AIOps can identify and alert teams to possible security breaches, using automated alerts and notifications for maximum efficiency and speed.

Automating compliance reporting and auditing

AIOps automates the collection, analysis and reporting of audit trails, regulatory requirements and security controls, enabling BFSI businesses to monitor and update their compliance workflows in line with regulations such as GDPR, PCI DSS and SOX. By shouldering the admin burden, AIOps not only simplifies compliance but also hugely reduces the risk of heavy fines and reputational damage.

Working in line with legislation

The Artificial Intelligence Act, which sets strict requirements for high-risk AI, encourages trustworthy innovation while protecting security. In line with this legal framework, AIOps boosts the safety and transparency of IT operations using AI and ML; supporting compliance through continuous risk monitoring and clear audit trails, ensuring robust data governance, and combining human oversight with manual overrides and explainable AI so that BFSI organizations can keep control over AI decisions.

Incident management.

On average, it takes 197 days to identify a breach and 69 days to contain one.

Such a massive delay can cost millions. Yet if a company can contain a breach in less than a month, they’ll save more than $1M compared to those on the other end of the spectrum.

AIOps is the latest and greatest tool to help with cloud incident management and resolution, speeding up breach identification with next-level automation capabilities and proactive analysis.

Analyzing incidents and offering resolution recommendations

Using powerful AI and ML technology, AIOps assesses the root causes of IT incidents by analyzing data from multiple sources and accurately pinpointing underlying issues. This generates actionable insights and recommendations for incident resolution, empowering IT teams to respond effectively. By providing precise diagnostics and tailored solutions, AIOps boosts the efficiency and speed of Cloud incident management, ensuring that disruptions are minimized and operations are quickly restored.

Automating processes to reduce mean time to resolution

AIOps automates routine incident response tasks, such as applying patches or reallocating resources. Not only does this boost accuracy and efficiency, but it takes on the admin burden from human engineers, freeing them up to focus on higher-value tasks. This also improves services for BFSI customers by minimizing the impact of incidents on day-to-day operations and enabling companies to meet SLAs consistently. Proactive incident management through AIOps allows BFSI firms to maintain service continuity, mitigate financial risks and boost customer trust by ensuring reliable and uninterrupted access to financial services.

A positive ROI.

81 percent of enterprises say that AIOps gives them a positive return on investment. In fact, almost half believe that the value of AIOps “dramatically” outweighs the costs.

The transformative impact of AIOps on the BFSI sector’s cloud operations is not to be underestimated. By automating compliance, enhancing security, optimizing performance and driving proactive incident management, AIOps isn’t just cost effective, it’s essential for operational resilience.

As BFSI organizations continue to invest in cloud technologies, AIOps will be key to success—giving financial organizations a competitive edge and boosting sustainable growth.

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The Challenge: AI’s Lack of Native Memory

AI models like Meta’s Llama 3 are increasingly being used in enterprise applications, but they have a critical limitation—no built-in memory. Managing chat history is complex, often requiring engineers to build custom solutions or rely on third-party tools. OpenAI’s Thread object has attempted to address this, but it lacks flexibility for those seeking more control over data retrieval and context persistence.

Why AI Memory Matters for Engineering Leaders

For engineering teams building AI-driven applications, effective memory management impacts:

• User Experience – AI agents that remember context improve engagement and reduce redundancy.
• Scalability – Poor memory handling leads to higher compute costs and inefficient processing.
• Data Control – Enterprise applications require transparent and structured knowledge retention beyond API constraints.

Zep’s Solution: Open-Source AI Memory with Graph-Based RAG

I’ve been pretty bullish on the future of AI models like Meta’s Llama 3 series and their inference stack. By making their platform widely accessible, Meta enables competitive innovation while pursuing platform and developer standardization—a natural fit for an industry moving toward small, customizable solutions. Yet, there remains a significant gap in the adoption of these models, leaving OpenAI to dominate with its industry-defining API standards on their own inference stack.

This open-source memory layer integrates with AI models to manage chat history and structure context dynamically. Unlike simple retrieval-based memory solutions, this system uses a knowledge graph approach, powered by Graphiti and Neo4j, to establish entity relationships and improve recall accuracy.

Key Differentiators of Zep’s Memory Layer:

• Graph-Based RAG (Retrieval-Augmented Generation): Enables precise and structured memory retrieval, improving AI responses.
• Session-Based Memory Persistence: Supports multi-user and multi-session environments, ideal for enterprise applications.
• Integration with Any AI Stack: Works independently of specific frameworks or inference engines.
• Structured Data Handling: Converts unstructured chat data into structured JSON for better downstream use.

Personal Experience with Zep

Personal AI has been a long-running project I’ve been exploring. I have several AI experiments that require long-form memory with the ability to continuously learn from Notion, synthesize knowledge, and maybe even one day execute tasks on my behalf.

Last month, I came across This foundational memory layer and agreed to do this sponsored article. It turned out to be exactly what I needed for my projects. Beyond offering memory, it’s built on a temporal reasoning layer powered by knowledge graphs. Best of all, it’s entirely open-source under a project called Graphiti, which leverages Neo4j.

Implementation: How Zep Works

For companies deploying AI models in production, integrating skilled AI engineers into teams can be just as important as choosing the right tools. At Torc, we’ve seen how access to specialized AI talent accelerates adoption of solutions like Zep and helps teams build more efficient memory architectures.

The platform can be integrated into AI workflows with minimal setup. Here’s how:

1. Create a User and Session: Establish unique identifiers for users and chat sessions.
2. Enable Memory Retrieval: Store and retrieve past interactions dynamically.
3. Leverage Graph-Based Context: Improve AI responses by structuring chat history as interconnected entities.
4. Deploy in Production: Use Zep’s cloud offering or self-hosted version for maximum control.

A simple TypeScript integration via the Vercel AI SDK allows for quick adoption, making it easy for teams to add long-term memory capabilities without overhauling existing infrastructure.

Why CTOs and Engineering Leaders Should Care

Scaling AI capabilities isn’t just about picking the right tools—it’s about having the right engineering talent to implement them effectively. With platforms like Zep offering flexible, open-source memory solutions, companies that leverage specialized AI engineers can optimize performance while staying agile. For companies deploying AI at scale, Zep provides:

• A more flexible alternative to OpenAI’s Thread object for context management.
• An open-source, vendor-neutral approach to AI memory, reducing platform lock-in.
• Better control over AI context and structured outputs for enterprise-grade applications.

Exploring Zep’s Knowledge Graph

This solution builds a knowledge graph to create a comprehensive view of the user’s world, capturing entities and their relationships. Let’s start by focusing on adding "Relevant Memory" and "Chat Messages" into our example project.

The system allows users to have multiple sessions contributing to the same shared memory. You can also connect multiple users as a group under one or even several sessions. Group-based sessions enable agents to access shared knowledge, such as documentation or group chats. Additionally, users with multiple sessions can maintain continuity in chats, even when logging out and back into an AI platform. It feels like Zep has all use cases covered.

The context management process describes how this platform handles memory when messages are sent. Zep dynamically creates relevant facts and entities using Graphiti’s knowledge graph. Each message from the user or response from the assistant updates the graph, enriching the memory’s context.

Final Thoughts

For engineering leaders exploring AI memory solutions, the right talent makes all the difference. If your team is scaling AI capabilities and looking for highly skilled engineers who understand retrieval-based memory and knowledge graphs, platforms like Torc can help accelerate deployment. Explore Zep, test its capabilities, and consider how the right team can drive even more value. Engineering leaders looking for an efficient, scalable way to manage AI memory should explore this solution without depending on rigid third-party APIs.

Want to learn more? Download our free nearshore guide

The Banking, Financial Services, and Insurance (BFSI) industry is facing unprecedented pressure to innovate. With evolving customer demands, increasingly sophisticated fraud, and complex regulatory landscapes, institutions are turning to Artificial Intelligence (AI) to drive transformation.

This shift isn’t just about keeping up—it’s about leading. The global AI in BFSI market, valued at $26.31 billion in 2023, is projected to skyrocket to $164.97 billion by 2033, growing at an impressive 20.15% CAGR. Yet, as Deloitte reports, enterprises remain cautious. Many BFSI organizations are taking a measured approach to AI adoption, ensuring they meet ROI expectations while addressing challenges like data governance, talent shortages, and infrastructure readiness.

Let’s explore how AI is reshaping BFSI analytics and the pivotal roles driving this transformation.

Key Challenges in BFSI Analytics and How AI Solves Them

• Risk Management: AI-driven credit models are delivering a 20% boost in predictive accuracy over traditional methods. Similarly, market risk systems using machine learning detect anomalies 30% faster and more precisely. These advancements enable institutions to proactively manage threats and safeguard assets.
• ‍Fraud Detection: Fraud costs the global economy over $5,13 trillion annually. AI-powered systems excel in detecting suspicious patterns across thousands of transactions in real time. These systems learn and adapt to emerging fraud tactics, reducing false positives and stopping threats before they escalate.‍
• Customer Insights & Personalization: Today’s customers demand hyper-personalized experiences—65% expect businesses to adapt to their changing needs. AI analyzes financial histories, transaction patterns, and even life events to deliver tailored recommendations, predictive alerts, and more meaningful engagements.
• ‍Operational Efficiency: By automating repetitive tasks like claims processing, loan approvals, and compliance checks, AI reduces turnaround times, minimizes errors, and allows employees to focus on strategic initiatives.

Emerging trends in AI for BFSI:

• Generative AI: Generative technologies, projected to grow from $1.38 billion in 2024 to $13.57 billion by 2032, are revolutionizing BFSI. They enable the creation of personalized customer content, automate marketing, and enhance operational workflows.
• Regulatory Compliance Automation: AI streamlines compliance processes, reducing costs and risks tied to manual oversight.
• AI-Driven Personalization: Institutions are leveraging advanced analytics to tailor products, optimize communication, and predict customer needs in real time.
• AI in Investment Strategies: Machine learning models analyze complex market data, giving portfolio managers sharper insights for smarter decisions.

The Talent Driving BFSI AI Transformation

The success of AI in BFSI hinges on having the right experts who combine technical expertise with domain-specific insights. Here are the key roles shaping the future:

1. Data Scientists
   
   • Build predictive models for fraud detection and risk analysis.
   • Skills: Statistical modeling, machine learning frameworks, BFSI regulatory expertise.
2. AI/ML Engineers
   
   • Deploy scalable AI tools for fraud prevention and compliance.
   • Skills: Python, cloud platform expertise (AWS, Google Cloud), scalable model deployment.
3. NLP Specialists
   
   • Extract insights from unstructured data like customer interactions or regulatory documents.
   • Skills: Sentiment analysis, chatbot deployment, handling text datasets.
4. Data Engineers
   
   • Ensure data pipelines are robust, secure, and reliable for real-time analytics.
   • Skills: SQL, data lakes, governance best practices.
5. Ethical AI Specialists
   
   • Address biases and ensure compliance with legal and ethical standards.
   • Skills: AI ethics frameworks, model audits, BFSI compliance.

A Strategic but Measured Approach

While AI is transforming BFSI, success isn’t about speed; it’s about precision. As Deloitte’s report highlights, 74% of enterprises meet their ROI targets for generative AI, but this success stems from being cautious and deliberate. BFSI organizations must balance innovation with robust governance, strategic hiring, and phased integration.Partnering with specialists like Randstad Digital | Torc ensures access to pre-vetted AI talent who deliver meaningful results tailored to the complexities of BFSI.

Building the right AI team

The BFSI industry is at a tipping point where AI is no longer optional. From fraud detection to personalized banking experiences, AI is redefining what’s possible. To stay competitive, financial institutions need the right people, strategies, and technologies to thrive in an AI-driven landscape. With Randstad Digital | Torc, you can connect with the experts who make AI transformation seamless and impactful.

Learn more about Nearshoring AI talent in LATAM. Download our free guide

In this episode of Guidance Counselor 2.0, host Taylor Desseyn welcomed his guest Anna Miller, a career coach and the founder of Code Career Mastery. She shared her expertise on networking, job searching, and how early-to-mid-level technologists can advance their careers. With more than a decade of tech and coaching experience, Anna’s insights were practical and actionable. Here are the top takeaways.

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‍Networking Is the Key to Career Growth

Anna emphasized that consistent networking is one of the most effective ways to advance your career. "Sending applications isn’t enough anymore," she said. Instead, she advises building meaningful connections by creating lists of people to reach out to, including bootcamp cohorts, past colleagues, and LinkedIn connections.

She also highlighted the importance of connecting, not just following, on LinkedIn. "You can’t send DMs or build relationships with someone if you only follow them," she explained. Instead, focus on starting conversations and maintaining them over time.

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Manage Your Online Environment

Anna introduced the concept of regulating your LinkedIn feed to maintain a positive and productive mindset. "If your feed is full of negativity or people feeling stuck, that will influence how you feel," she noted. Follow professionals who inspire you, share job opportunities, and discuss strategies for growth.

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Finding Clarity in Your Career Goals

One of the biggest obstacles Anna sees among technologists is a lack of clarity about their goals. "You can’t just say, ‘I want a job,’ and expect success," she said. She recommends researching job descriptions, understanding market trends, and identifying the roles and salaries you want. "It’s about aligning your skills and aspirations with the opportunities available," she added.

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Be Strategic in Your Search

Anna shared advice for job seekers:

• Create a Target List: Focus on companies that align with your goals and values.
• Reach Out Regularly: Consistent outreach and follow-ups are essential for building relationships and finding opportunities.
• Avoid Isolation: Don’t job search in a silo. Stay engaged with communities and connect with people who can offer guidance.

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Final Thoughts

Anna’s advice for her younger self? "Always ask for more money." It’s a reminder to advocate for yourself, stay informed about market trends, and approach your career with confidence and clarity.

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