Avoiding Financial Compliance Headaches with AI

Organizational risks fuel the fear of the unknown, which can cause organizations to unnecessarily invest time and money into manual processes or lack of processes that are neither efficient nor protect the corporation and its employees. Sometimes training and manuals are not enough to protect corporate value for financial institutions.

Not proactively mitigating risks in banking is a high-stakes game. A misstep could drum up serious compliance issues and lead to significant fines. Gartner estimates that up to 80% of enterprise data is unstructured. Within that data lies immense risk. This is why JPMorgan and Bank of America are earmarking millions annually for AI investments.

Artificial intelligence can protect your organization from risk by predicting issues long before an internal team can flag potential issues.

Conventional risk mitigation tools focus on an action that has already occurred, not what action might be actively happening. That distinct difference is where AI can predict a problem before it escalates into a disaster. By applying a cognitive computing approach, organizations can get ahead of problems long before they become a true risk.

Beyond risk management, the cost of staying in compliance and keeping up with regulations are at an all-time high. This has created a greater need across enterprises to get ahead of all internal and external risk before they affect the morale of the employees, the sales of organization and the reputation on the street. By tackling impending problems before they become a compliance headache, organizations can achieve incredible cost savings. This can be done through the application of highly sophisticated AI tools that puts a shield around the organization.

Culturally, employees want more from their company. Employees want to operate at a high-level without distractions of unacceptable behavior or insider risk. Artificial Intelligence is able to operate on multiple levels at once without further investment or privacy concerns.  

Identifying Risk Using Better AI Discovery Processes

Partnering with the right AI player is the first core component for managing and tackling risk that exists in and outside an organization. For example, working with an AI platform that targets incidents at the source — instead of after the fact — gives corporate teams time to resolve issues before they escalate. That is a core tenet of NexLP’s AI platform. NexLP’s AI platform alerts teams to compliance, litigation and human resource issues as they arise. This is done by targeting issues at the source.

By using past matter experience to build AI models, NexLP’s AI is uncanny in its ability to spot and remediate pre-existing conditions.. How? The adaptive AI model learns as it sees new data and incidents. Problems are addressed in advance — eliminating the need for humans to do guesswork.

The difference in an authentic AI approach is the application of cognitive analytics. By independently classifying millions of documents full of unstructured data ahead of a review team, organizations can quickly and efficiently identify risk factors.  For example, leveraging NexLP’s Story Engine — which uses AI and machine learning to derive actionable insight from structured and unstructured data — banking compliance and risk teams can learn where potential risk exists.

Which Tools are Useful for Identifying Risk in Banking?

Corporate risk teams shouldn’t have to worry about the application of artificial intelligence itself. AI experts like those at NexLP are already leading the charge, by building AI platforms, that  help organizations achieve operational efficiencies by leveraging the latest advances in natural language processing, cognitive analytics, and machine learning

These are just a few of the core tools that organizations can benefit from when applying a risk management approach to :

  • Network Analysis: Uncover better intelligence quicker by learning the context of conversations

  • Sentiment Analysis: Learn what people are saying and when to detect patterns

  • Pattern Detection: Discover what has changed, happened or emerged

  • Timeline Analysis: Identify events in near real-time, and in context other events

Collectively, these can help you gain a better sense of which risks might exist, and what steps need to take to mitigate those risk factors before they escalate into a larger issue. Getting ahead of the problem by using these four tools can help financial institutions focus on mitigation, instead of reactionary risk cleanup. By learning about potential problems before they actually exist, this helps internal teams be proactive about how they deal with issues. Manual intervention only deals with risk after it’s too late, which is why an automated AI approach is needed.

Risk Identifier Factors that Banking Compliance Teams Face

Litigation and regulatory infractions don’t just appear overnight. They are brewing quietly for weeks, months or longer. That’s why spotting these issues early and avoiding negative infractions. Dealing with an infraction after it happens, instead of before the potential infarction happens, is much more difficult and costly for any organizations’ bottom line and brand value.

Organizations must identify changes in behaviors and patterns, increased communication outside official channels, anomalies and internal factors that are against corporate policy. While many organizations rely on monitoring systems to reduce internal issues, these technologies fall flat in identifying risk early and being able to mitigate risk before it actually becomes a problem.

Having sound, established policies and a reliable alert system to spot/flag potential policy violations as they occur (in real-time) can keep organizations in compliance and helps you avoid the negative consequences that would ensue. Externally, this matters for protecting an organization as a whole. Internally, having the ability to, for example, stop harassment, bullying or other employment issues in their nascent state provides a better working environment, and are simply good business sense.

Risks Commonly Overlooked by Financial Institutions

Most corporations think illicit activity can be detected using keyword searches. Unfortunately, this is akin to throwing darts at a dartboard while blindfolded. Sometimes you’ll hit the bullseye. But most of the time, you’ll be off the mark. Many times the behavior is not spotted by the words, but rather by the actions and patterns of behavior, or even the lack of actions. Understanding what is the baseline activity, and then being able to assess variances in that behavior can be an invaluable tool for assessing whether an employee was retaliated against or whether harassment is occurring.  

Banking compliance and risk managers have so much data they should be leveraging to mitigate risk, but they do not have the tools to do so effectively or at scale. As a result, they run the risk that they won’t pick up on clues - implicit or explicit - to a brewing issue because they are simply doing keyword searches. Organizations are at risk for not knowing what is going on within their data, at risk for not seeing something right under their noses, and at risk for not acting on issues that could have been prevented within their organization.

How NexLP Helps Compliance and Risk Teams Mitigate Problems In and Outside Their Organization

Using NexLP’s Authentic AI, risk teams can learn from their past issues and proactively detect anomalies that exist within an organization’s datasets to create representative AI models. Those models will know who the executives are, what types of communication they are sending, product names, where and how you conduct business, office locations, etc, and you can use this information and experience you assess potential issues in new datasets.

Then, by utilizing NexLP’s Story Engine I3, AI models can patrol networks and alert you whenever a policy may be threatened. Once the risk is identified, NexLP’s technology helps the appropriate people address the risk by flagging it in real-time so that the real damage can be prevented or reduced, rather than by acting after the fact, which is far more costly and difficult to manage.

NexLP can uncover where risk exists by understanding all of the data and the context within which it lives. What is being said and done matters within a broader picture that only NexLP can see and piece together. As a result, NexLP can identify the true risks because our platform is built to understand the data holistically, within context and using state-of-the-art cognitive analytics.

Not staying up-to-date with the latest technology in the market isn’t a risk corporations should be taking today. Luckily, the NexLP team is already equipped with state-of-the-art technology that helps organizations avoid unnecessary risk and stay in compliance better than ever before.

How NexLP Addresses Proactive Risk Mitigation in Banking

NexLP is driving incredible efficiencies in banking by utilizing:

AI Models

  • These models are trained to know your business DNA. They learn things like:

    • Past Litigation/Investigation History

    • Reporting Requirements  

    • Backtesting

    • LIBOR exposure

    • Executive Profiles

    • Priv Workflows

    • Much, much more...

  • GDPR and PII Data Locator

    • Find, PII, PCI, PHI, etc

  • Pattern Recognition

    • Find patterns in your data no amount of lawyers can spot

  • Sentiment Analysis

    • Discovery negative sentiment and high pressure content immediately

  • Real Time Compliance Monitoring

    • Turn your past matters into Ai models that patrol your network spotting trouble

    • Put out the match before it turns into a forest fire

Jay Leib

Jay Leib is the CEO of NexLP, Inc. Jay has successfully founded and led software start-ups throughout his career. Most recently, Mr. Leib was at Chicago-based Relativity (formerly kCura) as the Chief Strategy Officer. Relatiivity is a Chicago based company focused on the eDiscovery vertical.