Reduce Operational Cost, Minimize Risk & Enhance Customer Experience through AI-based Financial Solutions.
The financial sector has been an early adopter of data analytics. In the last decade, Artificial Intelligence-powered by Machine Learning, Neural Networks, and Big Data Analytics has empowered financial organizations to derive intelligence from huge complex, and diverse datasets than ever before.
AI-based solutions are revolutionizing the way modern financial firms operate by addressing their critical needs such as customer service excellence, cost-effectiveness, and high security. Organizations leverage these solutions to streamline and optimize processes ranging from credit decisioning , fraud detection to customer support and financial risk management.
Transform Credit Risk Management through AI based credit risk assessment and monitoring solutions. Automate the process of profiling clients based on their risk profile and identify low risk, high value borrowers. Minimize risk and improve compliance through accurate estimation on how much to lend and generate early warning alerts for proactive risk management & compliance.
Real-time Fraud Detection Engine for automated analysis of transactional data and timely reporting of fraud attempts. Anomalies detection and fraud prevention through Neural network-based models that analyze customer profiles, behavioral patterns, and preferences. Identify suspicious merchants by automatically verifying the authenticity of their KYC documents by using computer vision and pattern matching algorithms.
Enhance operational excellence & customer experience by automating repetitive & manual workloads through AI driven applications. Eliminate human error and save significant costs by automating backend processes like account reconciliation, bookkeeping, and manual tasks such as extracting data, capturing documents, and cash management processes. Delight your customers by providing 24×7 personalized customer services across touchpoints through AI powered chatbots.
Improve the profitability of customer mix, lower customer attrition rate, and increase Customer Lifetime Value (CLTV) through machine learning algorithms that analyze customer attributes, behavior, and external factors to determine churn risk. Auto-recommendations for the most effective intervention to be applied for each customer based on the churn score.