Financial services analytics

This is an analytical solution that facilitates the efficient management of the loan application pipeline (for processors) by prioritising ‘qualified or eligible applications’ based on the availability of various documents from borrowers. The solution also provides micro-segment analysis at various attributes level.
Benefits

• Improve cycle time vis-à-vis loan application processing to enhance credit risk management
• Enhance productivity improvements on loan queues by up to 40%
• Reduce customer churn by up to 15%
• Boost conversion rate

This solution generates actionable insights by unlocking value from unstructured data of digital footprints left by banks’ customers on various social media channels, aiding in fraud detection. Such insights provide deep understanding of key focus areas for banks across products and services based on customers’ dissatisfaction drivers, impacting their profitability.
Benefits

• Help generate new targets and leads, as well as flag at-risk customers
• Improve campaign success rate by up to 20% through targeted data science strategies
• Reduce customer attrition by up to 15%
• Deliver key insights of the brand vis-à-vis its competitors
• Identify and measure social campaign performance

This customizable analytics solution helps identify potential mortgage funding leakage opportunity in correspondent mortgage lending business. It also provides a comprehensive dashboard with a view of key metrics such as ‘approved-versus-rejected loan percentage,’  ‘loan status,’  ‘time span,’  ‘reason code analysis,’  ‘lender’s rating,’  etc., throughout the loan process lifecycle.

• Maximise lending opportunities with reduced funding leakage
• Improve ratio of ‘approved-versus-rejected loan’ by up to 21%
• Identify potential opportunity savings for correspondent lending
• Reduce time span of the overall processes by up to 25%

This scalable and customizable analytics solution operationalizes and automates business operations performance reporting for critical service levels and key metrices. It creates better visibility for the leadership, managers, and supervisors on reporting performance for the previous day, week till date (WTD), and month till date (MTD) rolls across SLAs using data-driven metrics.
Benefits

• Generate accurate and on-time performance reports for timely review
• Reduce manual efforts by more than 98% in tracking critical service levels (CSLs) to improve the company's financial performance
• Empower managers to proactively implement mitigation plans
• Automated generation of reports in less than 1 minute using machine learning
• Offload ops bandwidth and capacity with the help of artificial intelligence

This replicable analytics solution entails setting up a mechanism for performance monitoring of the clients’ vendors by identifying and tracking key business metrices in an automated manner. It performs diagnostic analysis of vendor performance and generates insights for holding the vendors accountable.
Benefits

• Monitor key metrices to track vendor performance
• Reduce manual effort required for tracking vendor performance
• Improve service-level tracking with clear callouts on a daily basis

This solution provides a holistic view of key performance indicators (KPIs) in various fields of Financial Services, such as:

• Financial and regulatory reporting
• Retirement services/asset management
• Mortgage servicing
• Campaign management, mortgage, credit cards, CASA, etc.

• Deliver analytics insights to business users and enhance real-time decisions and effective interventions
• Automate monitoring of key metrices through data consolidation and rich visualization
• Reduce cost of report production significantly

Infosys Intelligent Event Bot engages with customers and offers personalized experiences based on daily life and everyday banking events. It acts as an intelligent advisor, assisting customers in making effective decisions using predictive analytics.

The following are Intelligent Event Bot use cases across business functions.

• Smart lead generation up by 5% for investment banks
• Personalized customer interactions
• 2X improvement in customer engagement
• Improved operational efficiency by 5% through organized work queues

The highest-value applications cluster around risk, customers, and operations. Risk analytics improves credit decisions, collections, and reserve forecasting. Customer analytics deepens understanding of needs, reduces churn, and personalises engagement across channels. Marketing analytics sharpens targeting and measures campaign return. Regulatory and compliance analytics supports reporting and reduces the cost of adherence. Operational analytics surfaces root causes, supports KPI-based decisions, and strengthens capacity planning. Specialised use cases such as complaints analytics, loan pipeline management, and revenue-leakage detection address specific pain points with measurable payback. The pattern is consistent: analytics delivers most where decisions are frequent, data is rich, and small improvements compound across large volumes.

Analytics strengthens risk and compliance by making decisions evidence-based and processes auditable. Credit and collections decisions improve when models draw on structured and unstructured data rather than rules alone, and reserve forecasting becomes more reliable. For regulation, analytics supports consistent reporting, monitors adherence, and flags anomalies early, which reduces the cost of compliance and the risk of penalties. Reputational risk is easier to manage when complaints and social signals are analysed for emerging issues. Because the same models and workflows run consistently, evidence for regulators and auditors is easier to produce. For a chief risk or compliance officer, analytics turns oversight from periodic effort into continuous, defensible control.

Managed analytics services improve outcomes by combining domain expertise, data science, and visualisation into a model that delivers decisions, not just data. Rather than building and staffing an analytics function internally, institutions access scientists, engineering, and tooling through a managed partner that scales with demand. The result reaches the business as sharper credit and pricing decisions, lower customer churn, more effective marketing, and faster, more reliable reporting. Centralising analytics also improves consistency and governance across products and portfolios. For leadership, the value is measurable: better margins, reduced risk, and improved customer experience, achieved without the cost and time of building deep analytics capability from scratch.

AI and machine learning are shifting financial services analytics from descriptive reporting toward prediction and automation. Models trained on structured and unstructured data improve credit decisions, detect fraud and anomalies, and forecast risk and demand. Natural language and generative tools accelerate analysis of complaints, documents, and social signals, while automation removes manual effort from data preparation and reporting. Infosys BPM applies the Topaz AI framework within managed financial services analytics, so these capabilities arrive as better decisions and faster insight rather than as platforms the institution must build and run. Human judgement stays central to lending, pricing, and risk decisions; AI sharpens the inputs and removes the manual load.