Artificial Intelligence/Machine Learning (AI/ML)

Artificial Intelligence and Machine Learning in BFSI

Transforming Banking and Financial Services through Intelligent Automation and Data Science

The Rise of AI and ML in Financial Services

Artificial intelligence (AI) and machine learning (ML) have emerged as critical differentiators for financial institutions seeking agility, efficiency, and personalization. As per Deloitte, nearly 70 percent of financial firms already use ML for fraud detection, credit scoring, and cash flow prediction. The Economist reports that 86 percent of financial executives plan to increase AI investments within the next five years, particularly in APAC and North America.

The COVID-19 pandemic further accelerated this trend, as global spending on AI soared from $50 billion in 2020 to a projected $110 billion in 2024. Clearly, AI and ML are no longer experimental technologies they are central to the future of the banking and financial services industry.

Financial institutions are adopting AI/ML to:

• Deliver more personalized customer experiences.
• Improve efficiency through automation and predictive analytics.
• Strengthen compliance, risk management, and decision-making.

Challenges Reshaping the BFSI Landscape

1. Ever-Changing Customer Demands

Modern customers expect instant, consistent, and personalized engagement. Studies show that 54 percent of consumers believe customer experience needs improvement, and 94 percent of banking firms fail to meet personalization expectations.

The gap between what customers expect and what traditional banks deliver is widening. E-commerce and digital-first players have raised expectations for frictionless experiences, and customers now expect the same in their financial interactions.

To stay relevant, financial institutions must deliver real-time, hyper-personalized experiences powered by AI enabling contextual engagement, predictive insights, and intelligent automation across all touchpoints.

2. Rising Competition from Big Tech and FinTechs

Technology-driven competitors are rapidly redefining financial services. According to the World Retail Banking Report 2020, 75 percent of tech-savvy consumers use at least one financial product from a Big Tech firm.

These new entrants leverage vast data pools, agile innovation, and advanced analytics to offer faster, simpler, and cheaper services. Traditional banks, constrained by legacy IT and siloed data, find it difficult to compete at this pace.

To stay ahead, banks must partner strategically with FinTechs or AI solution providers to modernize core operations and integrate innovation into existing frameworks.

3. Regulatory Pressure and Compliance

As AI continues to reshape the financial sector, regulators are tightening oversight. Compliance expectations now extend beyond data protection to include ethical AI use, algorithmic transparency, and explainable decision-making.

Institutions must strike a balance between innovation and accountability. A holistic approach to enterprise risk management and data governance is essential to maintain compliance while scaling AI initiatives.

Key AI/ML Use Cases in BFSI

1. Personalized Customer Experience

Banks are leveraging AI-driven natural language processing (NLP) and voice recognition to power chatbots, automate service requests, and personalize marketing campaigns.

• Predictive ML models tailor communication based on behavior and preferences.
• AI assistants enable frictionless interactions for customers with limited technical knowledge.
• Intelligent automation streamlines service delivery, reducing human effort while increasing satisfaction.

By anticipating needs and responding contextually, institutions can significantly improve engagement, loyalty, and retention.

2. Intelligent Credit Lending and Risk Evaluation

Credit underwriting has long been prone to human bias and inefficiency. AI now empowers institutions to automate underwriting and improve credit decisions through data-driven insights.

• Machine learning models analyze traditional and non-traditional data such as mobile usage and payment patterns.
• AI systems enable “explainable credit decisions,” ensuring transparency for regulators and customers.
• Automation reduces turnaround time and operational costs, enabling faster access to credit.

AI also makes lending more inclusive by extending credit to thin-file applicants and small businesses.

3. Risk Management and Regulatory Compliance

AI is transforming compliance through RegTech (regulatory technology) tools that identify, assess, and mitigate risks proactively.

• ML algorithms analyze structured and unstructured data from transactions to emails to detect anomalies.
• AI-powered surveillance systems can uncover insider trading or unusual trading behaviors in real time.
• Automated audit trails enhance transparency and reduce the risk of non-compliance.

This proactive risk management capability allows institutions to maintain trust and resilience in a heavily regulated landscape.

4. Preventing Money Laundering and Fraud

AI-powered fraud detection systems are redefining anti-money laundering (AML) processes.

• AI learns customer behavior patterns to flag anomalies and potential fraud in real time.
• ML-based tools reduce false positives, improving accuracy and operational efficiency.
• AI helps institutions comply with AML and counter-terrorism financing regulations by automating complex pattern detection.

The result is faster detection, lower losses, and stronger trust in financial systems.

5. Enhancing Customer Lifetime Value

Retention and cross-sell strategies depend on timing and relevance. AI/ML helps banks understand when to offer a product, what to offer, and to whom.

• Predictive analytics identifies life events or triggers that indicate buying intent.
• Recommendation engines create hyper-personalized offers, driving conversions.
• Advanced analytics optimize pricing, product bundling, and communication strategies.

AI turns data into actionable intelligence, maximizing lifetime value for both retail and corporate customers.

Building the Foundation for AI/ML Transformation

Strategy First

Scaling AI successfully requires clear objectives, strong leadership, and cross-functional collaboration. Research shows that firms with a strategic AI roadmap achieve five times higher ROI than those without one.

The transformation journey involves redefining how institutions harness data, measure performance, and integrate technology. A well-defined strategy ensures that AI adoption aligns with organizational goals and drives measurable business outcomes.

Data-Driven Culture

Data is the backbone of AI. Financial institutions are investing heavily in data analytics and intelligent automation to derive real-time insights from massive data volumes.

• According to IDC, 28 percent of financial organizations plan to direct major investments toward data, analytics, and AI.
• A full-cycle data management framework from capture to visualization ensures agility, compliance, and predictive insight.

By embedding a data-driven mindset across departments, banks can turn raw information into strategic assets that fuel innovation.

Cloud as an Enabler

The shift to cloud is accelerating the AI revolution in BFSI. Legacy systems often prevent scalability and slow innovation, whereas cloud-native infrastructure offers:

• Seamless integration of data sources.
• Lower maintenance and operational costs.
• Faster deployment of AI models across enterprise applications.

By breaking down silos and providing access to real-time data, the cloud enables institutions to adopt AI at scale while maintaining compliance and security.

Transforming BFSI with Newgen’s AI/ML Data Science Platform

Newgen’s AI/ML Data Science Platform unifies the entire data science lifecycle from model building to deployment in a single, intuitive environment.

Key Highlights:

• Unified workspace for both citizen and expert data scientists.
• Integrated coding support for R and Python alongside drag-and-drop workflows.
• End-to-end model management, including training, monitoring, and retraining.
• Deep MLOps capabilities for continuous improvement and governance.

This low-code platform simplifies the complex AI lifecycle, empowering financial institutions to accelerate innovation while ensuring compliance and performance.

The Road Ahead

Artificial intelligence is reshaping the core of financial services. As competition intensifies, institutions that can strategically scale AI will outpace their peers in innovation, efficiency, and customer experience.

AI and ML are no longer optional they are foundational capabilities that define how banks operate, engage, and grow.

With platforms like Newgen’s AI/ML Data Science Platform, financial institutions can unify data, intelligence, and agility to build the future of banking one that is faster, fairer, and deeply connected to customer needs.