Money-in-Motion
Banks face many challenges in today’s fast-moving business landscape. These include managing the massive influx of data due to digital transformation and digital payments, increasing cost of funds, losing revenue to fintech competitors, deteriorating asset quality and rising cost pressures affecting banks’ profitability, inefficient and redundant processes, and emerging risks from new and unexpected sources. By harnessing the transformative potential of data, banks can convert it into a strategic advantage that propels them beyond their rivals, enables expansion, and enhances profitability.
With LTIMindtree’s Money-in-Motion solution, banks can unlock the hidden potential of their data and gain a deeper understanding of customer behavior, preferences, and emerging trends. With this invaluable knowledge, banks can confidently make informed decisions and identify untapped avenues for revenue generation. This newfound edge can drive growth and increase profitability for the banks.
Money-in-Motion is a cloud-agnostic AI/ML-based solution catering to banks’ unique requirements. Banks can leverage their existing or preferred cloud service provider (Azure, AWS, or Google Cloud) and application architectures to maximize the value of existing investments and align with long-term technology strategy. The core solution’s driving force is integrating various advanced AI/ML models, combining their strengths to deliver unrivaled business functionality and efficiency.
Our Offerings
Classification of Payment Intent
Effectively categorize payment transactions into pay-ins and payouts for number of transactions and total dollar value. This assessment gives banks deeper insights and understanding of pay-in and payout amount, enabling informed decisions and implementation of targeted business strategies.
Categorization of PayOut Transactions
Unveil the intention behind the transactions by classifying payment data into meaningful categories such as vendor payouts, salaries, tax payments, and more. Using advanced Machine Learning (ML) techniques and Recency, Frequency, and Monetary (RFM) methodology, banks can unravel transaction patterns and behaviors to gain insights into customer preferences, fine-tune their business strategy, and deliver personalized product recommendations.
Entity Resolution
Segment counterparties into Existing-To-Bank (ETB) or New-To-Bank (NTB) categories using AI-powered entity resolution to enable targeted engagement and personalized offerings. AI-powered entity resolution enables banks to devise distinct tailored strategies, build stronger relationships, drive customer satisfaction, and capitalize on untapped opportunities to achieve sustainable business growth.
RFM Pattern Analysis
Use AI/ML algorithms on customers’ payment transactions to understand transaction behavior and spending patterns and to segment them into cohorts such as champions, loyalists, promising, about to sleep/churn, etc. This identifies high-value customers and uncovers potential churn risks, empowering banks to take proactive measures. They can deliver targeted and personalized offerings, ensuring effective engagement, fostering loyalty, and maximizing the lifetime value of each customer relationship.
Product Recommendation Engine
Revolutionize customer experience with personalized product recommendations using our AI/ML-powered models to seamlessly analyze the customers’ historical patterns and generate the right product recommendation with a propensity score for every customer. Money-in-Motion’s AI-enabled personalized product recommendation engine helps banks unlock the data’s full potential, deliver exceptional customer experience, and drive business growth.
Conversational AI
Implement decision augmentation using our Conversational AI capabilities to effectively utilize sectorial analysis models based on external marketplace data such as credit bureau ratings, industry trends, macroeconomic indicators, future industry trends, economic health, etc. This enables assessment of the potential risks and opportunities associated with specific lending scenarios, resulting in more accurate risk assessment and, ultimately, smarter lending strategies.
Speed to insights
Data-driven predictions
Interactive visualization
Hyper-personalized recommendations
Agile data exploration
Seamless integration
Decision augmentation
Business value
