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Part 3- Using Technology to Transform LEI, ISIN & Corporate Actions

This is the third part of a three part series in preparation of our webinar series. You can find part one here and you can find part two here.

For more information and registration, click the link below!

https://www.ptools.com/blog/webinar-series/process-automation-for-financial-services.html  

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Leveraging the Best Information For Discovery and Risk Mitigation

Before deciding what’s the “best” information, we should clarify who needs the information and why. Let’s start with some of the most common questions asked by investors, banks and government agencies:

  1. WHO am I ultimately working with when I do business with this financial institution?
  2. WHAT financial instruments have been issued on behalf of organization X.
  3. How can I effectively assess the risk of working with this financial institution?

While individually neither the ANNA database nor the LEI Index includes enough data to answer all three questions above, the combination of the two provides deep insight into the Who and What of financing and trading, helping organizations identify risk hotspots and make better decisions. Recognizing this, in 2019 ANNA and GLEIF launched a joint initiative to link and map the LEI and ISIN data and make it generally available. Let’s take a deeper look at how this data can help with discovery and risk mitigation.

  1. Who am I ultimately working with?

This is the foundational question that the LEI system was built to address; in fact GLEIF even uses the descriptive term “Who is Who” to make that purpose clear. More significantly, the LEI system also provides information on Parent and Child relationships between institutions. In GLEIF parlance this is “Who Owns Whom” and provides a level of transparency that heretofore was almost unattainable.

 legak-entity

(Image source: GLEIF)

The LEI model works well when all entities have an identifier; but that’s not always the case and other mechanisms may be required to reveal relationships between organizations. One of those mechanisms is to do a reverse lookup of financial entities using the ISINs listed for any one of the entities. This lookup process is considerably easier since ANNA data was added to the LEI Index in 2020.

Here’s an example of using this reverse lookup to identify banking relationships and history. Opus Bank, a regional bank in California is listed in the LEI Index. That listing shows another name (Bay Cities National Bank), but no linked LEIs.

opus-bank

  The listing in the LEI Index also shows a number of financial instruments (pulled in from the ANNA database). By working through the list of instruments we learn that some were issued by Cascade Bank in Everett, Washington - which was subsequently acquired by Opus. This gives us a jumping off point for deeper analysis of Opus Bank, with an eye to compliance and risk mitigation stemming from these acquisitions. In this scenario the cross-ref’ing and background data analysis can be done manually as there were very few transactions and events. But consider doing this for a financial institution like JPMorgan Chase with 187 children, or Morgan Stanley with almost 800 children. The only way to do this efficiently is through the use of technology, and open access to comprehensive data sets like the LEI Index and ANNA database.  

  1. WHAT financial instruments have been issued for this organization?

The ANNA database of financial instruments provides one of the most comprehensive views available of equity, debt and derivatives instruments across 120+ countries. In addition to providing the ISIN, the ANNA database also includes the CFI (Classification of Financial Instruments - ISO 10962) number for an entity, enabling investors, financial institutions and government agencies to identify not just the financial instrument but also the specific type. Some sample ISINs from the ANNA database are shown below.

apple-stock-1Sample ISIN - Apple Inc. stock offering

apple-stock-2

Sample ISIN - Apple Inc. bond, Japanese Yen

 

The addition of ANNA data to the LEI index has greatly simplified the process of identifying all the financial instruments associated with a family of entities, and reinforces the value of open data exchange. Again using Morgan Stanley as an example, there are over 9,000 instruments listed in the ANNA database across the 800+ Morgan Stanley children. With human actions accounting for over 80% of errors it’s easy to see why advanced technical platforms and open data access are critical in the analysis of these instruments. There simply is no other way to monitor compliance, inform investors and ultimately protect the institutions involved.

  1. How Can I Effectively Assess Risk?

While the data model used by the LEI Index and the ANNA database ties the WHAT (the instrument, identified via the ISIN) with the WHO (the entity, identified via the LEI), they don’t explicitly call out the financial institution that supported the issuing of the instrument. This institution data is essential for  effective risk assessment and mitigation, and is best obtained through analysis of the unstructured data contained in Corporate Action Announcements, proxy statements and 8-K filings. By combing through these documents using natural language processing (NLP) and AI tools such as those offered by pTools, risk management teams can build out a complete 360 view of WHO (the entity), WHAT (the instrument) and HOW (announcements) financial activities are being conducted.

Moving From a Reactive to Proactive Mindset

COVID-19 has sent shockwaves around the world, impacting every business and organization. Most organizations have focused on reducing their financial exposure, and retooling their business operations for new ways of work driven by social distancing. There has been a huge rush to digitize existing processes and reduce the amount of human effort involved. But rather than simply digitizing what was previously done manually, digital leaders are reimagining how transactions are executed and implementing complete systems that proactively reduce risk. 

In a real world example, Parent LOUs are frequently unaware that a child LOU had changed status, resulting in inconsistent data being stored within the LEI index, and potential legal, financial or regulatory penalties for non-compliance with KYC and AML requirements.

Features like automated entity extraction and data classification powered by natural language processing (NLP) and artificial intelligence (AI) can monitor news services, look for changes, generate alerts and even roll updates into the master systems of record. Reference data integration, SWIFT messaging codes and SRD II compliance can also be used to proactively complete data fields in ISIN and LEI submissions.

Closely related, it’s more critical than ever to capture the institutional knowledge that resides in the heads of seasoned employees before they opt out of the workforce. These seasoned workers are no longer in the office looking over the shoulders of new employees. And while they can, with their decades of experience, “have a gut feel” or “smell something fishy,” there is no digital counterpart for that. Or more correctly, simply digitizing the known processes will not provide the same level of quality and risk avoidance that you experience today. 

Conclusion

Whether you already have a digital system in place for processing LEIs and ISINs, or are just stepping into this space, there are significant benefits to using an integrated platform for ISINs, LEIs, and the analysis of Corporate Action Announcements. And the biggest “bang for the buck” comes about when organizations embrace a holistic approach to performance optimization, leveraging integrated platforms and the broadest possible data sets to optimize processes, reduce risk and accelerate transactions for customers. This is what will separate the digital leaders from the laggards in the years ahead.

 Register for the webinar with the link below!

https://www.ptools.com/blog/webinar-series/process-automation-for-financial-services.html