Announcing Quill for Anti-Money Laundering

November 10, 2015 Stuart Frankel

Quill for AML image

Two weeks ago, I was fortunate to attend the Bank of America/Merrill Lynch (BAML) Technology Innovation Summit where technology and business executives from BAML came together with private investors and industry professionals to discuss industry trends.

One key takeaway: dealing with regulatory compliance is a top strategic initiative for any financial services organization. In fact, the CIO of Bank of America’s #1 advice to technology providers attending the conference: "know the rules and regulations of our business."

The Post-Financial Crisis Regulatory Reality

Regulatory oversight for financial institutions has gotten intense and the fines and penalties for non-compliance have increased substantially. For Anti-Money Laundering (AML) in particular, enormous investments in tools, time and talent are being allocated to meet new regulatory demands.

Here’s a glimpse of the reality that banks have found themselves in post-financial crisis:

  • In 2014, U.S. and European banks paid nearly $65 billion in regulatory penalties and fines, about 40% greater than the previous year.1

  • Spending in North America to combat money-laundering activities has continued to rise at an average rate of 53 percent for banking institutions and is predicted to keep increasing.2

  • One bank states that nearly 75 percent of its analytics team’s time is spent on documentation rather than producing new detection models.3

Quill has always been an important asset to our clients’ compliance functions, as it automates and standardizes reporting processes, reducing the inconsistencies and inaccuracies that come with human error. We realized that this value-add from our advanced natural language generation platform is no longer a nice-to-have; it’s a necessity for those looking to comply with escalating regulatory mandates.

Quill for AML

Today, I am proud to announce Quill for Anti-Money Laundering. Quill for AML automates many of the manual processes related to regulatory reporting and compliance documentation by generating natural language reports that are consistent and traceable back to the system of record.

Our solution helps AML departments meet regulatory requirements by automating natural language reports (such as Suspicious Activity Report Narratives), ensure consistency and increase scale of reporting, and improve transparency by tracing the impact of model changes back to the system of record.

As we said in a recent white paper we published with Deloitte, Innovation Ushers in the Modern Era of Compliance   

Leading firms are turning to Advanced NLG systems to generate regulatory reports in order to explain and trace the logic of analytic platforms. Specifically, Deloitte is piloting the use of Quill to automate the narrative component of Suspicious Activity Reports (SARs) to assist with anti-money laundering compliance, provide detailed fraudulent activity reports for internal audit teams, and explain the impact that threshold changes and model tuning may have on analytic efforts.

We’re excited to be working with companies like Deloitte to apply Quill to the challenging tasks of fraud reporting and regulatory compliance.

http://www.wsj.com/articles/no-more-regulatory-nice-guy-for-banks-1419957394
KPMG Global Anti-Money Laundering Survey 2014
Conroy, Julie. Aite Group’s Global AML Vendor Report, 2015.


To learn more about Quill for AML, click the button below:

Previous Article
Employee Spotlight: Pia Opulencia
Employee Spotlight: Pia Opulencia

An interview with our Director of Product Management and her career at Narrative Science.

Next Article
Automating Personalization: Q&A with Zach Gipson, Chief Innovation Officer of USAA
Automating Personalization: Q&A with Zach Gipson, Chief Innovation Officer of USAA

Hear from USAA's Chief Innovation Officer about the importance of automating personalization within wealth ...

×

Get Narrative Science blog posts in your Inbox

Keep an eye out for your confirm email!
Error - something went wrong!