Solve banking's biggest challenges in fraud and financial crimes, risk management and customer experience with AI-powered solutions

Applying AI solutions like machine learning, large language models (LLMs) and agentic AI, either individually or in combination, can significantly accelerate, scale and optimize banks’ fraud and financial crimes detection and response capabilities. These technologies enhance incident management and mitigation, enable faster assessment of the health and effectiveness of fraud rules and models, strengthen Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance, and improve the customer experience through clearer communication and faster incident resolution.

The value of this solution:

• Improved outcomes.
• Fraud detection and prevention.
• Regulatory compliance.

AI techniques used in this solution:

• Machine learning.
• LLMs.
• Agentic AI.

How AI helps:

• Detect new and emerging threats through structured and unstructured data analysis.
• Assess the performance and integrity of existing fraud rules and models.
• Automate rule creation and model tuning based on real-time data.
• Streamline and automate KYC and customer onboarding workflows.
• Coordinate incident response and resolution.
• Improve customer communication and issue resolution.
• Support regulatory compliance with transparent and auditable processes.

The AI models provide:

• Real-time anomaly and pattern detection.
• Fast triage and review of flagged transactions and alerts.
• Ongoing analysis of alerts to evaluate fraud model performance.
• Automated case resolution or escalation workflows.
• Continuous model monitoring and dynamic rule refinement.
• AI-assisted document review and risk scoring during onboarding.
• Enhanced, responsive customer communications.
• Transparent and traceable decision making with full auditability and reporting.

AI solutions like machine learning, LLMs, NLP and agentic AI, applied individually or in combination, enable banks to significantly enhance the accuracy, speed and consistency of credit risk decisioning. These technologies support more precise risk assessments, enable real-time credit decisions, reduce manual workloads and improve both compliance and customer experience. With better data utilization and intelligent automation, institutions can optimize credit policies, improve portfolio performance and increase access to credit while managing risk effectively.

AI techniques used in this solution:

• Machine learning.
• LLMs (use case dependent).
• Agentic AI (emerging opportunity).
• NLP.
• Explainable AI.

How AI helps: 

• Assess borrower creditworthiness using structured and unstructured data.
• Automate credit scoring and loan decisioning based on dynamic risk models.
• Identify early signs of credit deterioration and portfolio risk.
• Streamline customer onboarding and credit approval workflows.
• Support real-time credit limit adjustments and loan pricing.
• Improve consistency and transparency in lending decisions.
• Enhance regulatory compliance with explainable and auditable models.

The AI models provide:

• Real-time credit scoring and decisioning based on comprehensive data inputs.
• Dynamic model updates using performance data and market signals.
• Early warning indicators and monitoring of at-risk accounts.
• Automated document analysis for income, identity and creditworthiness verification.
• Intelligent workflows for exception handling and policy overrides.
• Transparent credit decisions with clear rationale for regulators and customers.
• Enhanced customer engagement through faster approvals and tailored offers.
• Continuous improvement of risk models with feedback loops and performance tracking.

Automating machine learning model documentation reduces manual effort, increases consistency and ensures that updates are accurately captured throughout the model life cycle. This approach strengthens governance, improves transparency and supports regulatory compliance by simplifying audits and ensuring all model changes are recorded in a standardized and traceable format.

AI techniques used in this solution:

• Machine learning.
• LLMs (use case dependent).
• GenAI.
• Agentic AI (emerging opportunity).
• NLP.
• Explainable AI.

How AI helps: 

• Automatically generate documentation for each phase of the model life cycle.
• Track and log changes to models, including data sources, features and parameters.
• Create structured summaries of model behavior, performance and validation results.
• Support faster audit and regulatory reviews through searchable, explainable documentation.
• Improve model monitoring with real-time updates and feedback loops.
• Provide interactive dashboards and visual reports for reviewers and risk teams.
• Maintain version control and reduce inconsistencies across model deployments.

The AI models provide:

• Consistent, real-time audit trails of model changes and decisions.
• Automated tracking of version updates, data lineage and metadata.
• Goal-driven automated documentation orchestration.
• Reduced model fragmentation and improved visibility across the model ecosystem.
• Streamlined collaboration between data science, risk and compliance teams.
• Human-in-the-loop review to train model decisioning over time.
• Multi-agent collaboration for continuous compliance.
• Transparent model performance summaries for approval workflows.
• Enhanced adaptability to evolving regulatory change.

Synthetic data is a privacy-preserving technique that allows banks to generate artificial data that mimics real data. It can be used across the bank to support a variety of activities and opportunities like making more accurate loan decisions, testing fraud detection algorithms, better complying with regulations, or modeling significant events to better prepare for market fluctuations and potential crisis scenarios.

The value of this solution:

• Risk mitigation.
• Greater agility.
• Greater sustainability.
• Maximized operational efficiency.

AI techniques used in this solution:

• Synthetic data.

How AI helps:

Synthetic data helps banks better train models on a multitude of potential scenarios, improve credit decisions, transform their risk management and mitigation capabilities, better understand different fraud topologies, assess the business impact of significant events and deepen customer relationships.

The AI models provide:

Synthetic data provides the ability to test and model without having to worry about privacy concerns, compliance with information security regulations or impacting in-process business activities.

Customer complaint resolution is a critical component of customer experience and brand trust. AI solutions, including LLMs, GenAI, machine learning and platform analytics, can help banks accelerate and streamline their processes for receiving, interpreting and responding to complaints. These technologies can help improve employee productivity by reducing complaint resolution time and delivering more satisfying customer outcomes.

AI techniques used in this solution:

• LLMs.
• GenAI.
• Machine learning.
• NLP. 
• Trustworthy AI.

How AI helps:

• Understand and classify complaints based on context and urgency.
• Recommend relevant and personalized responses.
• Extract details from customer interactions to reduce the need for manual case review.
• Enable faster, more compliant complaint resolution across channels.
• Maintain compliance and transparency through explainable insights and audit trails.
• Reduce attrition by improving customer satisfaction.
• Potentially resolve minor customer complaint cases autonomously, freeing associates to tackle more challenging cases.

The AI models provide:

• Accurate, complaint classification and extraction of relevant case details.
• Real-time response recommendations.
• Complaint insights to inform operational or product improvements.
• Continuous learning to improve future resolution strategies.
• Transparent, traceable logic that supports regulatory and internal review.
• Enhanced control over communication workflows.

Analyze customer behavior, preferences and purchase history to provide hyper-personalized offers that boost satisfaction and sales. SAS integrated with an LLM helps banks efficiently analyze customer data to deliver the right offer at the right time, increasing next best offer (NBO) campaign success.

The value of this solution:

• Increased revenue.
• Increased customer engagement.
• Improved customer retention.
• Better customer experience.
• Increased customer satisfaction.

AI techniques used in this solution:

• GenAI is used to provide customized responses for campaigns, increasing the conversion rate and improving the efficiency of customer care executives.

How AI helps:

• Automatically generate customized offer messages and emails.
• Increase customer satisfaction and improve conversion rates with deep personalization.
• Increase customer engagement with relevant offers based on past behavior trends.
• Include AI-driven offer arbitration to send NBO to customers and incorporate this in the reply.
• Orchestrate the full decisioning process.

The AI models provide:

• Automatic highlighting of key relationships, outliers and more to reveal vital insights that inspire action.
• A level of transparency that empowers banks to have more control over communication.
• An audit trail for NBO product and solution selections for any changes in the model life cycle to accurately track updates.

Address the unique needs of each individual by gaining a deeper understanding of their behavior and preferences. AI helps banks leverage these insights to tailor more personalized recommendations and financial solutions to meet the needs of the customer where they are in their financial journey.

The value of this solution:

• Competitive advantages.
• Improved customer retention.
• Greater customer engagement.
• Increased customer satisfaction.

AI techniques used in this solution:

• GenAI can be used to analyze transactional data, banking transfer descriptions and customer pulse information.
• LLMs gather the meaning and context from large data sources.

How AI helps:

• Enhanced customer segmentation.
• Personalized financial advice.
• Improved market strategies.
• Increased customer satisfaction.
• Higher revenue and profitability.

The AI models provide:

• Automatic highlighting of key relationships, outliers and more to reveal vital insights that inspire action.
• A level of transparency that empowers banks to have more control over communication.