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

Banks can use machine learning and large language models (LLMs) to advance fraud and financial crimes detection, improve incident management and mitigation, assess the health and quality of implemented fraud rules and models and strengthen Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance.

The value of this solution:

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

AI techniques used in this solution:

• Machine learning.
• LLMs.

How AI helps:

• Faster and more accurate fraud detection.
• Precise assessment of the health and efficacy of fraud detection rules to better assess emerging threats and changing patterns.
• Streamlined customer onboarding that positively impacts AML and KYC activities.
• Improved and demonstrable compliance.

The AI models provide:

• Real-time capabilities to analyze data and identify anomalies that indicate fraudulent activity.
• Rapid inspection and assessment of generated alerts.
• Capabilities to scrutinize true positive/false positive ratio and other factors that help determine the health of fraud management rules.
• Continuous monitoring and analysis of fraud rule efficacy supporting agile adaptation to changing fraud challenges and tactics.
• Streamlined customer onboarding with automated document analysis and risk assessment to improve AML and KYC compliance.

Improve risk assessment accuracy and efficiency with GenAI. Risk managers can use data-driven AI insights that enable them to make informed decisions and manage risk effectively.

The value of this solution:

• Greater agility.
• Risk mitigation.
• Greater productivity.

AI techniques used in this solution:

By integrating SAS with a LLM, risk managers can generate behavioral scores, interpret and analyze rating degrades and upgrades, ingest the published article or report, use a retrieval-augmented generation (RAG) approach to check existence in previously published articles or reports, and orchestrate the full decisioning process. LLMs alone cannot solve the business task. They require solutions like SAS that provide complex process management, orchestration capabilities and governance.

How AI helps:

AI supports enhanced risk assessment, proactive early warning indicators and personalized risk evaluations. Using AI saves time and increases efficiency.

The AI models provide:

• The LLM is the link that supports the interpretation and analysis of rating degrades and upgrades and provides the opportunity to use a RAG approach to determine if a customer exists within a published article or report.

Automating machine learning model documentation reduces the time and effort of data scientists, ensuring consistent and complete updates. This approach improves governance and compliance by accurately capturing every change in the model’s life cycle and simplifying audits and regulatory reviews.

The value of this solution:

• Regulatory compliance.
• Maximized operational efficiency.
• Greater productivity.

AI techniques used in this solution:

• Machine learning.
• LLMs.

How AI helps: 

Classic text analytics or machine learning models can classify messages, perform sentiment analysis, or predict customer churn, enhancing LLM prompt accuracy. Further, LLM orchestration creates dashboards and interfaces that simplify reporting and approval while improving model monitoring for consistent, reliable results.

The AI models provide:

• Clear audit trails that reflect changes in the model life cycle and accurately track updates.
• A reduction in model fragmentation that often leads to inconsistencies, inefficiencies and blind spots.
• The ability to keep pace with changing regulations.

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 to model 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.

LLMs and GenAI paired with platform analytics help banks quickly and accurately resolve customer complaints. These combined technologies streamline complaint review and propose appropriate response options for associates to review and apply. This approach can significantly improve handling time, reduce customer service costs and improve customer experience.

The value of this solution:

• Faster issue resolution.
• Improved customer service.
• Maximized operational efficiency.

AI techniques used in this solution:

• LLM and machine learning algorithms are applied to gather the meaning and context from any text presented and classify the text appropriately.
• GenAI is used to provide customized responses and improve the efficacy and efficiency of customer care team members.

How AI helps:

• Better prepare associates to quickly respond to requests or problems.
• Increase customer satisfaction while maintaining data privacy and ensuring AI transparency.
• Strengthen customer decisions with insights from chat, email and social media.
• Proactively identify poor customer service/complaint situations.
• Reduce customer attrition/cancellation.

The AI models provide:

• The machine learning model pinpoints and extracts information, replacing the need for time-intensive review of manuals and guides.
• An explanation for why something happened, an examination of all options and the discovery of opportunities hidden deep in your data.
• A level of transparency that empowers banks to have more control over communication.

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.