보이지 않는 은행업의 중추: 매칭과 화해에 대한 심층 분석

Last year I celebrated two decades of immersion in IT, specifically within the Financial Services sector. During this period I have been a witness to remarkable transformations in banking and technology. The emergence of Fintech companies and their customer-centric
approach, along with significant advancements in software engineering like Agile methodologies, microservices, and cloud computing, have reshaped the landscape. Yet, intriguingly, the back-office operations of many financial service companies have remained
relatively static over these years, still grappling with 수동 인코딩, 반복 작업, Excel에 대한 과도한 의존.

금융 서비스 부문에서 특히 수동이지만 자동화 가능한 프로세스는 다음과 같습니다. 일치와 화해. This process arises in various forms, i.e. from identifying and addressing discrepancies (typically occurring due to issues
or gaps with the integrations) in master-slave integrations to correcting or removing duplicates and semi-automated updates of operational systems with data from external sources.

이용 가능 여부에도 불구하고 정교한 소프트웨어 (e.g. FIS IntelliMatch, Calypso Confirmation Matching, Misys CMS, Temenos T24 Confirmation Matching…​) for specific reconciliation tasks, such as payment and trade confirmation matching
(often based on SWIFT messages), the 대부분의 매칭 작업은 맞춤형 또는 수동 솔루션에 의존하는 경우가 많습니다., including Excel or even paper-based methods. Very often automation is also not pertinent, as matching is often involved in one-time actions
like marketing campaigns, data clean-ups, alignment with partners…​

더 나은 화해를 이해하려면 다음이 필요합니다. 구성 요소를 해부하다, ie

• 그것은 시작 비교를 위해 서로 다른 데이터 세트를 수집하고 변환합니다.. This consists of recuperating 2 data sets, which can be delivered in different formats, different structures, different scopes and with different names
  or enumerations. The data needs to be transformed to make them comparable and loaded into the same tool (e.g. a database or Excel), so that they can be easily compared.

• 다음 단계는 정확한 매칭 알고리즘. This can be a simple unique key, but it can also a combination of multiple attributes (composite key), a hierarchical rule (i.e. match first on key 1, if no match try on key 2…​) or
  a fuzzy rule (if key of data set 1 resembles key of data set 2 it is a match). Defining this matching algorithm can be very complex, but it is crucial in the ability to automate the matching and reach a good output quality.

• 일치 알고리즘이 정의되면 다음을 입력합니다. 비교 단계. For small data sets, this can be done quite simple, but for very large data sets, it can necessitate all kinds of performance optimizations (like indices, segmentation,
  parallelism…​) in order to execute the comparison in a reasonable time.

• 마지막으로, 확인된 불일치는 실행 가능한 결과로 변환되어야 합니다.보고서, 동료나 제XNUMX자와의 커뮤니케이션 또는 시정 조치(예: 차이점을 수정하기 위한 파일, 메시지 또는 SQL 문 생성) 등이 있습니다.

금융 서비스에서 매칭의 복잡성은 다양합니다. 우리가 탐험하자 몇 가지 일반적인 사용 사례 금융 서비스 환경에서:

• 대부분의 은행에는 증권 마스터 파일, describing all securities which are in position or can be traded at the bank. This file needs to be integrated with a lot of applications, but also needs to be fed by multiple data sources, like
  Telekurs, Reuters, Bloomberg, Moody’s…​ This means a security needs to be uniquely matched. Unfortunately, there is not 1 unique identifier describing all securities. Publicly traded instruments have a commonly agreed ISIN code, but private and OTC products
  like e.g. most derivatives usually do not. Banks have therefore invented internal identifiers, use fake ISIN codes (typically starting with an “X”) or use composite keys to uniquely identify the instrument (e.g. for a derivative this can be combination of
  ticker of underlying security, strike price, option type and expiration date).

• 소매 금융에서는 다음이 반드시 필요합니다. 특정 실제 인물을 고유하게 식별하고 일치시킵니다.. However even in a developed country like Belgium, this is easier said than done. Every individual in Belgium has a National Register Number,
  so this seems the obvious choice for a matching key. Unfortunately, Belgian laws restrict the usage of this number to specific use cases. Additionally this identifier is not existing for foreigners and can change over time (e.g. foreign residents receive first
  a temporary National Register number which can change to a definitive, other one later or in case of gender change the National Register Number will change as well). Another option is to use the identity card number, but this is also different for foreigners
  and will change every 10 years. Many banks therefore use more complex rules, like a matching based on first name, last name and birth date, but obviously this comes also with all kinds of issues, like duplicates, spelling differences and errors in the names,
  use of special characters in the names…​

• 매우 비슷한 문제는 회사 또는 더 구체적으로 매장과 일치. In Belgium, each company has a company number, which is similar to the VAT number (without the “BE” prefix), but this is again very national and 1 VAT number can
  have multiple locations (e.g. multiple stores). There exists a concept of a “branch number” (“vestigingsnummer” in Dutch), but this concept is not very well known and rarely used. Similar there exists the LEI code (Legal Entity Identifier) which is a code
  of a combination of 20 letters and codes, which uniquely identifies a company worldwide. Unfortunately, only large companies have requested a LEI code, so for smaller companies this is not really an option.
  Again more complex matchings are often done, like a combination of VAT number, postal code and house number, but obviously this is far from being ideal. In search for a unique and commonly known identifier, the Google ID becomes also more and more in use, but
  the dependency with a commercial company might also poses a big operational risk.

• 또 다른 흥미로운 사례는 VISA 카드 결제 시 승인 메시지와 결제 메시지의 일치. Normally a unique identifier should match both messages, but due to all kinds of exception cases (e.g. offline authorizations or
  incremental authorizations), this will not always be correct. Therefore a more complex rule is required, looking at several identifiers, but also to other matching criteria like acquirer ID, merchant ID, terminal ID, PAN (card number), timestamp and/or amount.
  이러한 종류의 일치는 사전 승인 완료를 이전 사전 승인과 일치시키거나 환불을 이전 구매와 일치시키는 것과 같은 다른 결제 사용 사례에도 적용됩니다.

• 거의 모든 비즈니스와 관련된 금융 사용 사례는 다음과 같습니다. 송장 및 결제 매칭. When a company issues an invoice, it needs to be able to see when the invoice can be considered as paid. This is important for the accounting, but also
  to see if reminders for unpaid invoices should be sent out.
  To uniquely match the payment with the invoice, in Belgium typically a structured comment is used in the payment instruction. This unique code with check digit provides a unique matching reference. Unfortunately, customers often forget to put the structured
  comment or use the wrong one (e.g. copy/paste of a previous invoice). This means a company needs to have a fallback matching rule in case the unstructured comment is missing or wrong. Typically a combination of payment amount, payment date, IBAN of counterparty
  and/or name of counterparty can give an alternative way to match those invoices.

보시다시피 매칭이 쉽지는 않지만 기본 단계를 이해하면 더 나은 매칭에 도움이 될 수 있습니다. 그 동안에는 Excel의 한계에도 불구하고 Excel은 (수동) 일치를 위한 강력한 도구로 남아 있습니다. 그러므로 quick reminder for everyone who wants
to do matching in Excel:

•  일치를 수행하는 VLOOKUP. 그러나 VLOOKUP에는 일치하는 항목이 없으면 오류가 발생하고 첫 번째 열에서만 검색할 수 있다는 점과 같은 특정 제한 사항이 있습니다. 강력한 대안은 다음을 사용하는 것입니다. 검색, 그
  does not have these limitations.

• 필요한 경우 복합 검색 키, 복합 검색 키를 사용하여 검색 데이터 세트에 열을 추가한 다음(예: "#"을 구분 기호로 사용하여 다양한 속성 연결) VLOOKUP/XLOOKUP을 사용하여 이 새 열을 검색합니다.

• 일부 주의점 VLOOKUP을 사용할 때:

  • 정확한 일치를 보장하려면 VLOOKUP 함수의 마지막 인수로 "false"를 추가하는 것을 잊지 마세요.

  • Ensure that data formats are the same. E.g. a number “123” and the text “123” will not match, so it is important to convert them to the same format first. Idem for identifiers starting with leading 0’s. Often Excel will convert those to numbers, thus removing
    the leading 0’s and not resulting in a match.

  • Excel에서 100.000개 이상의 행으로 구성된 데이터 세트를 사용하지 마십시오. 데이터 집합이 클수록 Excel의 성능과 안정성에 문제가 있습니다.
    대규모 데이터 세트에서 VLOOKUP을 사용하여 작업하는 경우 계산 모드를 "수동"으로 설정하는 것도 흥미로울 수 있습니다. 그렇지 않으면 데이터를 약간 변경할 때마다 Excel에서 모든 VLOOKUP을 다시 계산합니다.

  • VLOOKUP에는 세 번째 인수로 반환할 열 번호가 있습니다. 이 숫자는 열을 추가하거나 제거할 때 동적으로 조정되지 않으므로 열을 추가하거나 제거할 때 조정해야 합니다.

  • 일치만 원하는 경우 “=IF(ISERROR(VLOOKUP( , ,1,false), “일치하지 않음”, “일치”)”

이러한 트릭은 다음을 수행하는 데 도움이 될 수 있습니다. 수동 매칭 속도를 높이세요, 그러나 분명히 실제 자동화가 항상 더 좋습니다.

금융서비스에서의 매칭은 다각적인 도전, but understanding its fundamental steps is key to improving outcomes. While tools like Excel offer temporary solutions, the future lies in intelligent automation, which can significantly
streamline these processes. For those seeking to delve deeper into matching complexities or automation, leveraging advanced tools and platforms, including AI-driven solutions like ChatGPT, can provide both insights and practical solutions.

내 모든 블로그를 확인하십시오. https://bankloch.blogspot.com/

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• 출처: https://www.finextra.com/blogposting/25297/the-unseen-backbone-of-banking-a-deep-dive-into-matching-and-reconciliation?utm_medium=rssfinextra&utm_source=finextrablogs