AI-Driven Bank
Middle Office (loan/deposit ops/processing)
Retail
Commercial
Reg & Risk (Compliance & BSA/AML)
Small Business
Treasury Management
IT (Data, DevOps)
Back Office (servicing)
InfoSec/Security
Identify Regulatory Change for Policy, Standards, and Procedures.
The process of detecting, interpreting, and integrating regulatory changes into policies, standards, and procedures is predominantly manual. This traditional approach is slow and may lead to delays in compliance, potentially exposing the firm to regulatory risk.
Problem Statement
AI could update a threat catalog for security. Compiling a comprehensive repository of potential threats that an organization might face, categorized and assessed for their impact and likelihood. Examples output of this use case includes a continuously updated log of adversarial threats, including emerging threats related to AI/ML that are added to the log as they are discovered.
Description
Bank Function
Identify Regulatory Change
Difficulty in offering relevant products to diverse customer profiles.
AI analyzes customer data and behavior to predict and offer the most relevant banking products to each customer.
Cross-sell
Intelligent Cash Management
Inefficient cash management for commercial clients.
AI optimizes cash management for commercial clients by forecasting cash flow and recommending investment opportunities.
Assessing credit risk accurately for large loans.
AI models analyze vast amounts of data to predict creditworthiness, improving loan decision accuracy and speed.
Credit Risk Assessment
Streamlining the loan approval process.
AI algorithms quickly assess risk factors and financial health to speed up the loan approval process for small businesses.
Loan Approval Process
small business
Identifying fraudulent transactions among vast numbers of legitimate ones.
AI systems analyze patterns to detect anomalies that may indicate fraud, enhancing security and trust.
Fraud Detection in Transactions
Optimizing cash reserves without compromising liquidity
AI predicts cash flow trends, helping businesses manage their liquidity efficiently by suggesting optimal cash positions.
Liquidity Management
AI could update a threat catalog for security. Compiling a comprehensive repository of potential threats that an organization might face, categorized and assessed for their impact and likelihood. Examples of this use case include a continuously updated log of adversarial threats—including emerging threats related to AI/ML that are added to the log as they are discovered.
Regulatory Risk & Compliance
There is no standardized automated process available to validate the correct implementation of controls. Teams or consultants currently attempt to manually confirm compliance with the controls, which is inefficient and susceptible to human error.
To improve the validation of control implementation, GenAI can be used to create scripts for control system integration. Built on a foundational LLM vector representation of existing controls, GenAI could generate test scripts to validate the implementation of new controls within systems, ensuring that controls work seamlessly when combined with other system components.
Control Validation Test Scripts
Manual due diligence is time-consuming and may miss critical information.
AI conducts enhanced due diligence by analyzing vast amounts of data for red flags and anomalies.
Enhanced Due Diligence
Product owners are required to fill out comprehensive impact assessments for their products, a process that can be complex, time-consuming, and prone to inaccuracies. This manual process increases the risk of oversight and non-compliance with data protection and privacy regulations.
GenAI could be used to identify and propose potential issues related to data management that may lead to control breaks within the firm's operational risk management framework. The chatbot's role is to augment the ongoing data risk management processes by highlighting potential risks for consideration. Identify potential data management-related control breaks within the firm operational risk management framework. Supplement the existing ongoing process to propose potential issues that should be considered in scope for risk review. The tool would not be used exclusively for any definitive decision making but recommend relevant issues for further review.
Data Management Risk ChatBot
IT (Data & DevOps)
AI continuously monitors network traffic and flags anomalies, indicating potential issues or breaches.
Manual monitoring of IT infrastructure is not scalable.
Anomaly Detection in Networks
Manual code reviews are time-consuming and may miss vulnerabilities.
AI performs automated code reviews, identifying potential security vulnerabilities and code quality issues.
Automated Code Review
Creating board reports is incredibly time-intensive and manual. It requires lots of people across several teams to pull together board reporting from data that comes from multiple systems/sources. This process is prone to human error.
GenAI could be used to aggregate data from several systems, map it to standardized template(s), draft language based on change from previous reports, and identify potential areas to highlight given events in the marketplace.
Board Report Generation
middle office
Manual compliance reporting is prone to errors and inefficiency.
AI automates the generation of compliance reports, ensuring accuracy and timely submission.
Automated Compliance Reporting
Managing and assessing various financial risks.
AI provides deep analytics to assess market, credit, and operational risks, aiding in more informed decision-making.
Risk Management Analytics
Current contract comparisons are manual or dependent on Word comparison functions. Redlines can be made but cognitive assessments as to whether pertinent information is missing or something was included that shouldn't be must still be done through time-consuming tasks.
GenAI could complete the redline of differences between contracts, look for standard language that is not included, identify terms that may be too strict, and compare contract content across vendors for any major discrepancies (inclusions or exclusions).
Compare Contract Iterations
back office
Automating repetitive and manual back-office tasks.
AI-driven robotic process automation streamlines operations, reducing errors and improving efficiency.
Process Automation (RPA)
AI tools automate the extraction and analysis of information from documents, speeding up processing times and reducing manual work.
Processing large volumes of documents efficiently.
Document Processing and Analysis
Threat modeling for products is a critical but resource-intensive process. This manual approach can limit the scope and frequency of threat assessments, potentially leaving emerging threats unaddressed and creating gaps in the organization's security posture.
Threat Catalog Creation
Security/infosec
AI forecasts future risks from historical data. Activities include data collection, model building, risk forecasting, and trend analysis. This use case can be used on a platform, product, or application level to drive insights on predictive risks for technology owners to use and proactively allocate resources to mitigate and harden controls before risks are realized.
There is a challenge with prioritizing risks effectively, as traditional methods may not fully capture the dynamics of emerging threats or the evolving risk landscape. Without a prioritized risk management approach, critical vulnerabilities may remain unaddressed, leaving the firm exposed to potential threats.
AI algorithms continuously learn from network traffic and past incidents to detect and respond to threats in real-time.
Identifying and mitigating cybersecurity threats.
Cybersecurity Threat Detection
