Realm for Finance

Privacy & Security Challenges in Modern Banking

In today's digital banking landscape, financial institutions face unprecedented challenges in balancing data-driven innovation with privacy requirements. As open banking initiatives expand and financial ecosystems grow more interconnected, organizations must find new ways to leverage sensitive data while maintaining regulatory compliance and customer trust.

Cross-institutional Fraud Prevention

Need to collaborate on fraud detection while protecting sensitive customer data and transaction histories.

Secure Credit Risk Assessment

Requirement to share credit information across organizations while maintaining data privacy and regulatory compliance.

Privacy-Preserving Analytics

Need to leverage customer data for service improvement while ensuring GDPR compliance and data minimization.

Regulatory Reporting & Compliance

Complex requirements for data handling under GDPR, CCPA, and sector-specific regulations while maintaining operational efficiency.

Our Solutions for the Financial Industry

PAMOLA: Privacy-Preserving AI for Finance

PAMOLA is a cutting-edge Privacy AI Studio empowering financial institutions to unlock the full potential of their data while ensuring ironclad privacy and regulatory compliance. Generate synthetic data for model training, anonymize sensitive information for analysis, and protect customer data with advanced techniques like differential privacy and homomorphic encryption. PAMOLA enables secure data collaboration, reduces the risk of data breaches, and accelerates innovation in a privacy-first world.

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AYITA: Your Intelligent AI Assistant for Finance

AYITA is an AI-powered assistant designed to revolutionize how financial professionals work. Leveraging Retrieval Augmented Generation (RAG), fine-tuning, and intelligent AI agents, AYITA delivers hyper-personalized insights, automates complex workflows, and facilitates secure enterprise collaboration. From generating insightful reports to answering complex financial queries, AYITA empowers your team to make data-driven decisions faster and more effectively.

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Industry Use Cases

Churn Prediction and Personalized Offers

Financial institutions can leverage PAMOLA to generate high-quality synthetic data that mirrors real customer behavior. This synthetic data can then be used to train sophisticated churn prediction models without compromising customer privacy. AYITA's AI agents can automate the delivery of personalized offers and recommendations to retain valuable customers.

Privacy-Preserving Training: Train accurate churn models using synthetic data, protecting sensitive customer information.
Proactive Retention: Identify at-risk customers and proactively offer personalized retention incentives.
Automated Personalization: Automate the creation and delivery of targeted offers and recommendations.

Fraud Detection through Collaborative Analysis

Banks, retailers, and telecom companies can collaborate using PAMOLA's Secure Multi-Party Computation (SMPC) capabilities to analyze transaction data for fraud detection without revealing sensitive details. Federated learning enables the development of shared fraud detection models that are more robust and accurate.

Improved Detection Rates: Identify fraudulent activities more effectively by combining insights from multiple sources.
Enhanced Privacy: Protect sensitive transaction data while contributing to collaborative analysis.
Cross-Industry Collaboration: Foster secure data sharing between different sectors to combat fraud.

AI-Powered Credit Scoring with Explainability

Financial institutions can use PAMOLA's Federated Learning (FL) capabilities to develop advanced credit scoring models that leverage data from multiple sources while preserving privacy. AYITA can then be used to provide clear explanations for credit decisions, enhancing transparency and complying with fairness requirements.

Transparent Decisions: Understand the factors driving credit scoring decisions with AI-powered explainability.
Fairness and Compliance: Ensure credit scoring models are free from bias and comply with regulatory requirements.
Improved Customer Trust: Build trust by providing clear and understandable explanations for credit decisions.

Personalized Financial Advice with LLMs

AYITA, leveraging the power of Large Language Models (LLMs), can provide personalized financial advice to customers based on their individual financial situation, risk tolerance, and goals. AYITA can analyze vast amounts of financial data and generate tailored recommendations for budgeting, saving, investing, and retirement planning.

Tailored Recommendations: Receive personalized advice that aligns with your specific financial needs and goals.
Data-Driven Insights: Benefit from AI-powered analysis of your financial data and market trends.
24/7 Availability: Access personalized financial advice anytime, anywhere.

Investment Research and Analysis with LLMs

AYITA can assist financial analysts and investors by automating time-consuming tasks such as investment research and analysis. By leveraging LLMs, AYITA can analyze financial news, market reports, and company filings to identify trends, risks, and opportunities. This empowers financial professionals to make more informed investment decisions.

Automated Research: Save time and resources by automating investment research and analysis.
Comprehensive Insights: Access a wide range of data and insights to support investment decisions.
Enhanced Efficiency: Improve the efficiency of investment research and analysis processes.

Data Anonymization for Regulatory Reporting

Financial institutions can use PAMOLA to anonymize sensitive customer data before sharing it with regulators or other third parties for reporting purposes. This ensures compliance with data privacy regulations while still enabling valuable insights to be extracted from the data.

Regulatory Compliance: Meet data privacy requirements by anonymizing sensitive information.
Data Utility: Preserve the analytical value of data while protecting individual privacy.
Reduced Risk: Minimize the risk of data breaches and privacy violations.

Compliance & Security

The financial sector operates under strict regulatory frameworks where data privacy, security, and AI governance are critical. Realm’s solutions ensure compliance while enabling secure and innovative data-driven insights.

Data Privacy, Anonymization & Compliance

Financial regulations such as GDPR, CCPA, PIPEDA, and Basel III impose strict data protection requirements. PAMOLA ensures compliance through synthetic data generation, differential privacy, and advanced anonymization techniques, reducing the risks associated with personal data exposure. By leveraging these methods, financial institutions can conduct deep analytics without compromising customer privacy.

Secure AI & Federated Learning

Financial institutions must protect data while enabling analytics. PAMOLA’s federated learning (FL) and secure multi-party computation (SMPC) allow collaborative AI model training without sharing raw data, ensuring both privacy and security.

Explainable & Fine-Tuned AI with Local Models

AI-driven financial models must be interpretable, adaptable, and secure. AYITA enables fine-tuning of local AI models, ensuring transparency and regulatory compliance while allowing organizations to train models on-premises without transferring sensitive data to external servers. This approach enhances security while maintaining high model performance across different financial environments.

Secure Data Collaboration

Financial organizations need to exchange insights securely. PAMOLA’s privacy-preserving analytics ensure that sensitive data remains protected, while institutions collaborate on fraud detection and risk management.

Explore Our Financial AI Solutions

Discover how REALM’s AI-powered finance solutions help institutions enhance data security, risk management, and fraud detection. Our latest brochure covers Private Set Intersection, Federated Learning, and AI-driven financial insights.

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