Regulatory and Legal

The ability of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) platforms in processing large volumes of data with speed and accuracy is transforming regulatory compliance and legal operations.

Application of AI in compliance systems has already demonstrated three clear benefits for regulatory compliance and legal teams – reducing false positives, lowering costs, and addressing human error.

1. Reducing false positives – Large banks experience false positives in their compliance systems at alarmingly high rates. Well-constructed AI and ML solutions capture, analyze, and filter thousands of data elements, and can be trained to intelligently reduce false positives that waste banks’ time and money every day. AI can improve the efficiency of compliance and fraud detection operations and reduce costs significantly. 

2. Lowering costs – Most regulatory and legal processes require significant manual intervention to ingest, cleanse, analyze, process, extract, and prepare data for consumption for downstream applications. AI and ML platforms automate the ingestion process and simplify the workflow at very high accuracy levels, even for documents with variable formats, which results in increased efficiency and substantially lower costs compared to current operations.

3. Addressing human error – Whether attributable to poor due diligence, outdated technology, or ineffective processes, human error costs regulated industries billions every year. The constantly evolving regulatory landscape for all industries requires compliance officers to track, manage, and analyze detailed data about transactions, customers, and operational activities, such as at large banks. The sheer volume and complexity of even obtaining this information in a trusted manner makes it an impossible task that is by nature error prone. AI and ML are the best enablers available today to manage the scale of datasets that must be analyzed in an auditable, secure manner.

AI and related technologies have proven to be very effective in interpreting unstructured and qualitative data outputs, identifying complex, non-linear patterns in large data sets, improving the accuracy of calculations, and reducing complex data sets to simpler or more tractable forms. These capabilities enable applications that require reading large sets of documents, such as legal agreements, to look for specific trends, clauses, or conditions for business applications such as the forthcoming Libor Transition in Financial Services, or Data reviews in Investment Banking, or Legal Research and Due Diligence for case preparations.

The Journey:

Haystream helps demystify the application of AI for regulatory risk, compliance, and legal applications by:

1. Cataloging, assessing, and defining available content: This includes all the relevant datasets, internally and externally, in a variety of data stores, in multiple formats and languages. Often there are security access restrictions, and iterative versions with a history of changes that must be considered, particularly in legal agreements.
2. Problem Definition: For regulatory and legal applications, what is often needed is review and search through many different documents or agreements to find specific laws, regulations, and conditions whose applicability to a current situation needs to be determined. This may require searching in multiple datasets within and outside the organization. Additionally, a similar search and assessment is often needed to identify where these conditions are applicable, with the goal to identify and mitigate any potential financial or legal risk to the company. Most such operations are manually intensive, and still results in significant errors and fines, as is witnessed by fines assessed on major companies for non-compliance with a spectrum of regulations including KYC, GDPR, MDR, among others.
3. Creating and Implementing the Solution: Haystream helps companies leverage AI and related technologies to implement a robust solution that automates data management for large datasets, at a high accuracy level, to reduce such errors. More new tools and databases to handle it (such as NoSQL and Graph) have been emerging in recent years. Large volumes of data and sophisticated algorithms require thoughtful definition and powerful processing capabilities. Haystream will guide your organization develop the right solution for your business requirements in a cost-effective manner.

The Strategy:

Haystream has a smart automation approach to help your organization apply the right set of data management principles and AI techniques to create trusted data sets, and automate manual, time-consuming tasks such as data ingestion, tagging, searching, document review, and analysis so that your compliance and legal experts can focus on using their expertise for organizational benefit instead of rationalizing impossibly larger datasets.

• Data Management to ensure that the data being used is comprehensive, trusted, secure, and the right version.
• AI, ML, NLP and domain specific technology platforms to ingest, extract, and analyze data with a well-defined tagging mechanism that enables searching, with business, domain, and geographic context.
• Text analytics and insights – Processing unstructured data, and/or identifying relevant content, negative news, case notes and more.
• Using Knowledge Graphs and other semantic tools to assess cross-entity relationships, and deep, hidden connections for applications such Know Your Customer (KYC), Fraud Detection, and Credit Risk Management.
• Using automation technologies such as Robotic Process Automation (RPA), Reconciliation Engines, AI-led techniques, or other domain specific platforms to increase straight through processing rates.

Application of AI in Legal Operations:

The Legal industry can reap substantial benefits by applying AI-led technologies to their business processes. Some of the key areas of potential application include:

• Capturing and accessing all current laws and interpretations

• An AI-powered search engine reduces the time and manpower needed to research and support case construction, report similar or related cases and overturned rulings to eliminate dead ends, and all 50 states statutes and regulations.

• Side-by-side comparison and review of dozens or hundreds of documents in all sorts of contract, M&A, employee review, or similar situations that are the bread and butter of legal work. Built-in AI models can look for anomalies among versions, compares clauses across docs, identifies key data points within docs, and can be used, for document review.

• Leverage NLP to review and analyze contracts according to rules assigned by the user to quickly sign and execute a document covering conflict resolution, common contract review tasks such as due diligence, general commercial compliance, lease abstraction, ISDA schedules, GDPR, and more.

• Several repetitive tasks like templated NDAs, in-taking request for legal services, or routing of risk assessment templates to appropriate hands.

• ML techniques can be leveraged to make sense of massive public and private databases to power KYC, global watchlist, document verification, address, email, device, and phone risk, and models to identify identity fraud and synthetic fraud.

• ML powered risk assessment is part of regulatory compliance in KYC. Risk may enter the process at any stage from client underwriting to claims and ultimately fraud investigations optimizing “straight through processing” which minimizes human intervention and total time from claim to award.