No Result
View All Result
SUBSCRIBE | NO FEES, NO PAYWALLS
MANAGE MY SUBSCRIPTION
NEWSLETTER
Corporate Compliance Insights
  • Home
  • About
    • About CCI
    • Writing for CCI
    • NEW: CCI Press – Book Publishing
    • Advertise With Us
  • Explore Topics
    • See All Articles
    • Compliance
    • Ethics
    • Risk
    • FCPA
    • Governance
    • Fraud
    • Internal Audit
    • HR Compliance
    • Cybersecurity
    • Data Privacy
    • Financial Services
    • Well-Being at Work
    • Leadership and Career
    • Opinion
  • Vendor News
  • Career Connection
  • Events
    • Calendar
    • Submit an Event
  • Library
    • Whitepapers & Reports
    • eBooks
    • CCI Press & Compliance Bookshelf
  • Podcasts
  • Videos
  • Subscribe
  • Home
  • About
    • About CCI
    • Writing for CCI
    • NEW: CCI Press – Book Publishing
    • Advertise With Us
  • Explore Topics
    • See All Articles
    • Compliance
    • Ethics
    • Risk
    • FCPA
    • Governance
    • Fraud
    • Internal Audit
    • HR Compliance
    • Cybersecurity
    • Data Privacy
    • Financial Services
    • Well-Being at Work
    • Leadership and Career
    • Opinion
  • Vendor News
  • Career Connection
  • Events
    • Calendar
    • Submit an Event
  • Library
    • Whitepapers & Reports
    • eBooks
    • CCI Press & Compliance Bookshelf
  • Podcasts
  • Videos
  • Subscribe
No Result
View All Result
Corporate Compliance Insights
Home Compliance

Is Artificial Intelligence Ready for Financial Compliance?

by Daniel Fernandez
September 19, 2017
in Compliance, Featured
hand pushing "regulatory compliance" button on keyboard

Key Challenges and Benefits of AI

Machine learning and artificial intelligence have become buzzwords in the financial services industry. Daniel Fernandez helps to break down the difference between the two terms and explains how these technologies are being used by compliance departments today. By delving into how these technologies work, Daniel sheds light on the issues they can help to solve, the challenges facing their increased adoption and what financial institutions should be doing right now to take advantage.

Like many industry buzzwords, artificial intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is this: AI has become an overloaded and misused term, often mistaken for machine learning (ML). This post aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today and highlight how ML can immediately add value in financial compliance applications.

In simple terms, artificial intelligence enables computer systems to perform tasks that require human intelligence; intelligence is the key word. In contrast, machine learning refers to a computer system that has the ability to learn how to do specific tasks and in some instances can use past data to make future decisions or predictions without being explicitly “told” (programmed) how to do so. Machine learning is a key building block of artificial intelligence.

Contextualizing Machine Learning

AI and ML are often confused because the terms are used interchangeably. But they are not the same. Today, ML is used in many narrow compliance applications, including risk detection models and other event classification use cases. A narrow ML application, however, does not constitute AI in the context of compliance.

That said, a combination of systems and programs (based on ML) could constitute an artificially intelligent system, although no such systems truly exist in the compliance realm today.

Most artificially intelligent systems use a combination of machine learning applications and techniques along with rule-based systems (to be fully interactive). For example, phone-based smart assistant applications (such as Google Now, Siri, Cortana and Alexa) use a set of application components which are mostly powered by machine learning. These include: language identification, translations, transcriptions, natural language understanding, etc. In these interactions, you can perform tasks, such as booking a cab, where the following steps are performed behind the scenes:

  1. Understand your command of requesting a car service and your destination;
  2. Detect your location and determine your optimal pick-up location;
  3. Reach a nearby driver and agree with this driver on taking this trip;
  4. Communicate back to you with an estimated fare for final confirmation.

Interacting with a smart assistant in this manner can be considered AI because the smart assistant fully replaces a human (the taxi dispatcher), who would normally perform these tasks. Thus, human interaction is bypassed altogether.

Why Compliance Needs Hybrid Intelligence

While taxi dispatchers can be replaced by AI, the same cannot be said for compliance analysts. And this is a good thing, because while smart machines and complex algorithms can process a lot of data to automate and perform some human tasks faster, there are limitations. For example, the current machine-learning models and advanced statistical techniques can process far more messages, trades and records than humans can, but humans are still needed to review, apply judgment and make decisions about what constitutes or does not constitute compliant communications. Why?

First, such decisions involve substantial operational and financial risk, as well as potential severe legal consequences and reputational damage. Secondly, true AI systems need to learn what is good (compliant) and bad (noncompliant) behavior, and there simply aren’t enough instances of noncompliant communications in a firm’s data for an AI machine to learn this distinction and make reliable decisions.

So when it comes to financial communications compliance, while technology can eliminate the time-consuming tasks entailing large data analysis, it is no substitute for the decision-making abilities of the compliance analyst, at least not today.

The Data Integration/Aggregation Challenge in Machine-Learning Technologies

Adoption of ML technologies is accelerating across many industries (financial services firms included), thanks in large part to a renewed focus on applied problems and sharing of research findings. Still, one of the biggest challenges of ML remains unsolved. Machine learning relies on data.

Most advanced analytics projects devote a large portion of time to identifying and curating the necessary data to feed advanced algorithms. But are the analytics engines themselves flexible enough to handle all of these data inputs? In some cases, the answer is “no,” as some ML systems are constrained to accepting only certain types of data in certain types of formats.

This exact concern was highlighted in a recent report by Jeffries, a global investment banking firm. The research report focused on issues related to IBM Watson. It highlighted an example of a health sciences project that required a significant amount of services and effort to integrate data sources from different systems and did not produce the desired results.

According to the Jeffries report, for example, MD Anderson has already spent over $60 million dollars on a Watson project and stated that “IBM is very ‘picky’ about the data it feeds Watson.” The project has since been halted because of the extensive integration that would have been required to make it work with MD Anderson’s systems.

As part of their normal course of business, financial Institutions already have to archive and analyze large amounts of data. By implication, ML platforms that rely on restrictive data inputs only make this problem worse. Financial institutions would need to store separate data in different formats in order to support advanced analytics models.

In communications compliance, this becomes a much bigger problem due to the large variety of structured and unstructured data sources. Financial firms need to weigh the pros and cons – should I invest in a one-off ML project requiring custom integration, or should I layer ML on top of my existing surveillance solution? A lot of work already goes into making data useable for the surveillance process; why not leverage the data your organization has already organized and curated for ML as well?

Either way, the expectation needs to be that ML is not intended to be self-sufficient. It needs to work hand-in-hand with a human compliance analyst, at least for today.

Fit for use is another challenge these projects face: the issue of the usability of the data was also highlighted in the MD Anderson/Watson audit. The report concluded that the Watson system was “not ready for human investigational or clinical use.”

Just having an ML system spit out results is not enough. You have to be able to integrate these results into the day-to-day supervision and investigative workflow of your compliance analysts in a way that’s intuitive and contextual. This ensures that your ML is not just creating “more noise,” but instead providing useful information for decision-making.

The Expanding Role of Machine Learning In Compliance

In today’s financial regulatory environment, numbers alone are not useful to compliance analysts. Consider, for example, under regulations such as MAR and MiFID II (which will soon go into effect), it’s no longer sufficient to just monitor for actual fraudulent trading practices; firms also need to monitor communications for “intent to commit market abuse” throughout a trade or transaction life cycle. This necessitates obtaining additional context surrounding monitored users and their respective activities. This might include behavior anomalies, relationship discrepancies or other fluctuations in communications or trade data.

The expectation for such analysis is also being driven by regulators such as the SEC (Securities Exchange Commission), which has already started to incorporate these techniques into their daily compliance processes as well. Scott W. Bauguess (Acting Director and Acting Chief Economist DERA from the SEC) discussed this in a presentation at the OpRisk North America (in June 2017) when he said that the SEC uses “unsupervised algorithms to detect patterns and anomalies in the data, using nothing but the data.”

When it comes to financial communications compliance, machine-learning technologies can truly improve the compliance process, but only if they fit into your firm’s current workflow. There’s no such thing as true AI in financial compliance (at least not yet), but ML can enhance your compliance team’s view of monitored users, help to detect financial communications compliance issues and – if implemented and applied correctly – facilitate analyst decision-making.


Tags: Artificial Intelligence (AI)Financial ServicesMachine LearningRegTech
Previous Post

Building a Culture of Compliance Through Training

Next Post

The Slippery Slope to an Eroded Culture of Compliance

Daniel Fernandez

Daniel Fernandez

Daniel Fernandez is the Analytics Product Manager for NICE Financial Communications Compliance. Currently working towards driving cutting-edge analytics solutions in the financial services compliance space, Dan has extensive experience in agile product development in startup environments and large enterprises, as well as experience with large data warehouse deployments in financial services for market data, trading, pricing and risk data capture. While in the finance industry, he worked at a major investment bank focused on a proprietary data capture and analytics framework for both real-time and historical data use cases. With a background in both finance and management information systems, he possesses deep technical understanding, as well as an understanding of business drivers for strategic data applications.

Related Posts

The Future of E-Governance and Anti-Corruption Efforts

The Future of E-Governance and Anti-Corruption Efforts

by Corporate Compliance Insights
March 21, 2023

In today’s complex and rapidly changing business environment, managing risk and ensuring compliance is more important than ever. The integration...

DALL·E 2023-02-16 13.18.43 - magritte style painting of robot looking into mirror

A Bot Isn’t Going to Take Your Place, But AI Will Make Your Job Harder

by Jennifer L. Gaskin
March 8, 2023

OpenAI’s splashy ChatGPT rollout has generated untold amounts of text, both directly and indirectly. While much of what’s been written...

Writing Effective Audit Observations

Writing Effective Audit Observations

by Aarti Maharaj
February 28, 2023

The key to writing an effective audit observation is having a comprehensive structured process. The Institute of Internal Auditors recommends...

Overtime Computations for Nonexempt Employees

Overtime Computations for Nonexempt Employees

by Aarti Maharaj
February 13, 2023

Employers face unique challenges when calculating overtime for non-exempt employees, whether paid hourly, piece work, or salaried. Payroll professionals must...

Next Post
glass sided office building with one broken window

The Slippery Slope to an Eroded Culture of Compliance

Compliance Job Interview Q&A

Jump to a Topic

AML Anti-Bribery Anti-Corruption Artificial Intelligence (AI) Automation Banking Board of Directors Board Risk Oversight Business Continuity Planning California Consumer Privacy Act (CCPA) Code of Conduct Communications Management Corporate Culture COVID-19 Cryptocurrency Culture of Ethics Cybercrime Cyber Risk Data Analytics Data Breach Data Governance DOJ Download Due Diligence Enterprise Risk Management (ERM) ESG FCPA Enforcement Actions Financial Crime Financial Crimes Enforcement Network (FinCEN) GDPR HIPAA Know Your Customer (KYC) Machine Learning Monitoring RegTech Reputation Risk Risk Assessment SEC Social Media Risk Supply Chain Technology Third Party Risk Management Tone at the Top Training Whistleblowing
No Result
View All Result

Privacy Policy

Founded in 2010, CCI is the web’s premier global independent news source for compliance, ethics, risk and information security. 

Got a news tip? Get in touch. Want a weekly round-up in your inbox? Sign up for free. No subscription fees, no paywalls. 

Follow Us

Browse Topics:

  • CCI Press
  • Compliance
  • Compliance Podcasts
  • Cybersecurity
  • Data Privacy
  • eBooks Published by CCI
  • Ethics
  • FCPA
  • Featured
  • Financial Services
  • Fraud
  • Governance
  • GRC Vendor News
  • HR Compliance
  • Internal Audit
  • Leadership and Career
  • On Demand Webinars
  • Opinion
  • Resource Library
  • Risk
  • Uncategorized
  • Videos
  • Webinars
  • Well-Being
  • Whitepapers

© 2022 Corporate Compliance Insights

No Result
View All Result
  • Home
  • About
    • About CCI
    • Writing for CCI
    • NEW: CCI Press – Book Publishing
    • Advertise With Us
  • Explore Topics
    • See All Articles
    • Compliance
    • Ethics
    • Risk
    • FCPA
    • Governance
    • Fraud
    • Internal Audit
    • HR Compliance
    • Cybersecurity
    • Data Privacy
    • Financial Services
    • Well-Being at Work
    • Leadership and Career
    • Opinion
  • Vendor News
  • Career Connection
  • Events
    • Calendar
    • Submit an Event
  • Library
    • Whitepapers & Reports
    • eBooks
    • CCI Press & Compliance Bookshelf
  • Podcasts
  • Videos
  • Subscribe

© 2022 Corporate Compliance Insights

Welcome to CCI. This site uses cookies. Please click OK to accept. Privacy Policy
Cookie settingsACCEPT
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT