AML (Anti-Money Laundering:マネーロンダリング対策) 規制は継続的に強化され、金融機関に大きな影響を与えています。規制への対応が難しく、審査の精度や頻度の低下を招くことがあります。その結果、金融機関は大きなリスクを負うことになり、数十億ドル相当の罰金を科せられる可能性があります。これは金融機関が何もしていないからではなく、十分な対応ができていないからなのです。
Recipe for Success: Keeping Up with AML Regulations Using AI
Anti-money laundering (AML) regulations are continually getting stricter and dramatically impacting financial institutions. Firms are having trouble keeping up with ordinances, so they are not screening as accurately or as often. As a result, they are taking on a large amount of risk that may result in billions of dollars worth of fines. But these fines aren’t because the institutions aren’t doing anything about it. It’s because they aren’t doing enough.
To combat these challenges, firms can throw people at the problem and scale with a complex network of multi-tier analysts to sift through results — or they can process as much as they can, hoping no one notices how far behind the team is or how few of the required checks are being done. But now that technology and AI are at our disposal, institutions can try some of the innovative ways companies are using technology to scale their operations to confidently stay ahead of the requirements.
How Did We Get Here?
Central to AML and know your customer (KYC) regulations is the mandate to match new and existing customers with the latest watchlists from regulatory bodies around the world. A seemingly simple task is incredibly complex because the data being compared is inherently messy. It is vital for financial organizations to ensure that systems and procedures are in place to correctly identify and flag the names of these individuals – taking into account variations in spellings, word order, languages, writing systems, and other factors in order to be compliant. When an institution doesn’t have a good system in place, the volume of flagged results requiring human review is unmanageable. This can lead to inadequate manual screening or corners being cut in the process.
For financial institutions to provide screening in a timely manner, they must expand and optimize their current technologies at scale to remain relevant and connected to their customers. Many forward-thinking institutions have already begun implementing AI-driven technologies to not only deliver faster, more accurate results with fewer errors, but also possess the inherent scalability to meet the ever-increasing complexity and volume of names on external and internal watchlists.
Use Case: How Vital4 Optimized Name Matching by Replacing “Regular” Search with “Fuzzy Name Search”
When your institution relies on searching for people and businesses, you need to be sure you’re getting it right from the get-go. Vital4 is a company that offers a globally accessible due diligence data search and found traditional keyword-based search did not provide the accuracy that they sought to provide. This was mission-critical.
Unlike regular search, fuzzy matching for names is very niche. Misspellings in documents like typing “aslo” instead of “also” are easily identified and corrected by standard search engines. But when it comes to names, “Cyndy” vs “Cindy” could either be a misspelling or a completely different person. That makes a big difference.
Vital4 added two powerful capabilities to enhance their search — intelligent fuzzy name matching and AI-powered tagging of people and businesses in articles. They natively process text in 20+ languages to automatically identify entities (people, places, locations, etc.) in each document, so that a search for “Paris Hilton” (person) does not hit a document about the Hilton hotel in Paris (place).
Use Case: How Refinitiv Understands People and Organizations with Name Matching Technology
When financial institutions and regulated businesses rely on you to provide highly curated information from trusted sources, name searches and understanding people and organizations
is a discipline in itself. Refinitiv understood the gravity of this and went on a search for a solution to add high accuracy with flexibility to evolve with the ever-changing regulatory requirements.
Refinitiv needed more than a simplistic name matching tool for their product, Refinitiv World-Check One, so they implemented a solution that has multiple layers of name-centric knowledge built into its algorithms and knows how to phonetically search in and across those languages. Refinitiv World-Check One can now tell when a name is out-of-order or has been shortened. All of this name intelligence is funneled into a name match scoring algorithm that also thinks like a human, only better because it’s both consistent and explainable.
When it comes to financial compliance screening, nuanced information and controls are powerful tools to reduce reputational risk and increase efficiency that is impossible with simple search.
How Your Institution Can Improve its Watchlist Screening Process
AML compliance is a highly complex problem with intricacies that are constantly in flux. Associating a person’s name with all of its different variations is no trivial task and one which must be done with careful consideration and accuracy.
Basis Technology’s Rosette Name Indexer (RNI) allows for the most accurate means for resolving names across languages and scripts. Organizations can increase the coverage and accuracy of searching for foreign names and more readily become compliant within the labyrinth of the regulatory environment.
So if you’re interested in optimizing your AML compliance efforts, book your demo today.