Banking & Finance Solutions

Transform your financial data management with AI agents designed specifically for the banking and finance industry.

Key Challenges in Banking & Finance

Financial institutions face unique data management challenges that our AI agents are specifically designed to address

Regulatory Compliance

Financial institutions must comply with complex & evolving regulations like GDPR, BCBS, FINRA, and more, requiring meticulous data management.

Automated compliance checks

Comprehensive audit trails

Legacy System Integration

Banks often operate with a mix of legacy systems and modern platforms, creating complex data integration challenges.

Seamless data mapping

Format standardization

Data Quality & Accuracy

Financial transactions and customer data require the highest levels of accuracy and consistency to prevent costly errors.

Automated data validation

Continuous quality monitoring

Key Challenges in Banking & Finance

Financial institutions face unique data management challenges that our AI agents are specifically designed to address

Compliance Automation

Our GovernAI agent automatically enforces regulatory compliance requirements across your data ecosystem, ensuring consistent adherence to financial regulations.

Automated GDPR, BCBS, and FINRA compliance checks

Comprehensive audit trails

Legacy System Integration

Our MappingAI and TransformAI agents work together to seamlessly integrate data from legacy systems with modern platforms, ensuring consistent data flow across your organization.

Automated schema mapping between systems

Standardize formats for consistent data.

Data migration between legacy & modern systems.

Data Quality Assurance

Our CleanseAI and ReconcileAI agents continuously monitor and improve data quality, ensuring the highest standards for financial transactions and customer information.

Automate detection & resolution of data quality issues

Cross-system data reconciliation for financial accuracy

Continuous data quality monitoring and reporting

Business Intelligence

Our DiscoverAI agent uncovers valuable insights from your financial data, helping you make better business decisions and identify new opportunities.

Customer behavior pattern identification

Risk assessment data analysis

Fraud detection pattern recognition

Success Story

Financial institutions face unique data management challenges that our AI agents are specifically designed to address

Compliance Automation

Digital Transformation Initiative

Read full case study

Challenge

Global Banking Corporation needed to migrate customer data across 15 different systems as part of a digital transformation initiative. The project involved complex regulatory compliance requirements, legacy system integration, and strict data quality standards.

Solution

We deployed our suite of AI agents to automate the data migration process, with special focus on compliance, data quality, and system integration. Our managed services team provided continuous oversight to ensure optimal performance.

Result

70%

Reduction in migration timeline

92%

Decrease in data quality issues

$4.2M

Cost savings

Use Case

Customer 360 Profiling

Objective 

Create a unified, realtime view of each customer to drive personalized offers and risk monitoring.

Instructions

Retrieve all active customer IDs and core demographics (name, DOB, gender, marital status).

Fetch KYC verification status, expiration dates, and flag missing or expired records.

Pull address (state, city, pincode) and contact details (phone, email).

Get account opening date and type, calculate customer tenure and age.

Outcome

A consolidated 360° profile for every customer, complete with KYC status, contact information, and tenure metrics.

Impact Compared to Traditional Approach

Speed: Profiles generated in minutes instead of manual data consolidation over days

Completeness: Eliminates siloed records by merging disparate data sources

Personalization: Enables targeted campaigns and risk alerts based on uptodate profiles

Loan Risk Assessment

Objective

Evaluate the creditworthiness of loan applicants to accelerate decisions and reduce default rates.

Instructions

Fetch credit bureau scores, existing loan counts, and outstanding balances.

Retrieve monthly EMI obligations and check for any missed or late payments.

Calculate sixmonth average account balance, total inflows, and salarycredit trends.

Derive each applicant’s debttoincome ratio and flag those above risk thresholds.

Apply rulebased filters for autoapproval, manual review, or rejection.

Outcome

A risk classification and preapproval status (AutoApprove, Review, Reject) for each applicant.

Impact Compared to Traditional Approach

Efficiency: Credit decisions in seconds vs. days of paperwork and manual checks

Consistency: Rulebased approvals replace subjective underwriting judgments

Risk Control: Early flagging of highrisk applicants reduces nonperforming loans

Fraud Detection

Objective 

Automatically identify and prioritize suspicious transactions to minimize losses and streamline investigations.

Instructions

Retrieve six months of debit and credit transactions, including timestamps and channels.

Summarize transaction volumes by merchant category and geolocation.

Flag anomalies: unusually large amounts, geolocation mismatches, highfrequency bursts, oddhour activity.

Crosscheck transactions against blacklists of merchants, IP addresses, devices, and identities.

Consolidate flagged cases into a ranked alert list for the fraud team.

Outcome

A prioritized fraudrisk report listing suspicious transactions with severity scores and reason codes.

Impact Compared to Traditional Approach

Proactivity: Detects fraud in near real time vs. endofmonth batch reviews

Accuracy: Uncovers complex anomaly patterns beyond manual ruleofthumb checks

Cost Savings: Reduces investigation workload and financial losses by focusing on toprisk cases

CrossSell Opportunities

Objective 

Identify highpotential customers for targeted financial product offers, boosting conversion and wallet share.

Instructions

Pull account and transaction history: balances, debit/credit counts, and volumes over six months.

Analyze top spending categories (e.g., travel, retail) and frequency of channel usage (ATM, UPI, POS).

Score each customer on eligibility for products (travel insurance, premium cards, loans).

Recommend the most suitable product category per customer.

Export segmented customer lists with contact details for campaign execution.

Outcome

Campaignready lists of customers ranked by crosssell eligibility, with tailored product recommendations.

Impact Compared to Traditional Approach

Relevance: Delivers personalized offers vs. broad, untargeted mailings

Conversion: Increases response rates through datadriven matching

ROI: Optimizes marketing spend by focusing only on higheligibility segments

ATM Optimization Strategy

Objective 

Optimize ATM network placement and cash management to improve customer access and reduce costs.

Instructions

Gather 90day ATM transaction counts, average withdrawal amounts, peakhour patterns, and downtime logs.

Classify ATM locations by urban/rural category and cluster by proximity.

Identify underutilized (low transaction volumes) and overutilized (high cashouts) machines.

Recommend relocation, adjusted cashreplenishment schedules, or maintenance for flagged ATMs.

Compile operational reports for network planners.

Outcome

Actionable ATM network plan with relocation and maintenance recommendations.

Impact Compared to Traditional Approach

Visibility: Realtime usage data replaces periodic manual surveys

Cost Efficiency: Reduces cashout incidents and idle capacity

Customer Experience: Ensures ATMs are available where and when needed

Salary Account Utilization Analysis

Objective 

Assess salary account profitability and design retention or upgrade strategies.

Instructions

Retrieve salary account holder demographics and tenure, plus salary credit amounts over six months.

Analyze account usage: withdrawal patterns, bill payments, transaction volumes by channel.

Score accounts into profitability tiers based on credit and usage behavior.

Recommend retention actions (offers, RM outreach) for lowtier accounts and crosssells for medium/high tiers.

Generate dashboard views and export action lists for relationship managers.

Outcome

Profitability tier classification and targeted retention/upsell action plans for salary account holders.

Impact Compared to Traditional Approach

Precision: Datadriven tiering vs. onesizefitsall retention campaigns

Revenue Growth: Increases fee income and crosssell uptake

Churn Reduction: Proactive engagement lowers attrition among key segments