銀行業務の目に見えないバックボーン: マッチングと調整の詳細

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 手動エンコーディング、反復的なタスク、Excel への依存度の高さ.

金融サービス部門における特に手動でありながら自動化可能なプロセスは、次のとおりです。 マッチングと調整. 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.

利用可能であるにもかかわらず、 洗練されたソフトウェア (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 照合タスクの大部分はカスタムまたは手動のソリューションに依存することがよくあります, 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…​

より良い調整を理解するには、次のことが必要です そのコンポーネントを分析するすなわち、

• それはで始まる 比較可能にするために異種データセットを収集および変換する. 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.

• 次のステップでは、 正確なマッチングアルゴリズム. 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.

• マッチングアルゴリズムが定義されたら、次のように入力します。 比較フェーズ. 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.

• 最後に、 特定された矛盾は実用的な出力に変換する必要があるレポート、同僚や第三者へのコミュニケーション、修正措置（相違点を修正するためのファイル、メッセージ、または SQL ステートメントの生成など）など。

金融サービスにおけるマッチングの複雑さは多岐にわたります。 探検してみましょう いくつかの典型的な使用例 金融サービスの分野では:

• ほとんどの銀行には 有価証券マスターファイル, 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).

• リテールバンキングでは明らかに次のことが不可欠です。 特定の物理的な人物を一意に識別して照合する. 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…​

• 非常によく似た問題は、 企業、より具体的には店舗のマッチング. 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.

• もう一つの興味深い事例は、 VISAカード支払いにおけるオーソリゼーションとクリアリングメッセージの照合. 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.
  この種の照合は、事前承認の完了とその前の事前承認の照合、または払い戻しと以前の購入の照合など、他の支払いのユースケースにも当てはまります。

• ほぼすべてのビジネスに関係する財務上のユースケースは次のとおりです。 請求書と支払いの照合. 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.

ご覧のとおり、マッチングは決して簡単ではありませんが、基本的な手順を理解することでマッチングを改善することができます。 それまでのところ、制限はあるものの、Excel は依然として (手動) マッチングのための強力なツールです。 したがって、 quick reminder for everyone who wants
to do matching in Excel:

• 　 照合を実行する VLOOKUP。 ただし、VLOOKUP には、一致しない場合にエラーが発生したり、最初の列でしか検索できないなど、特定の制限があります。 強力な代替手段は、 XLOKUP、その
  does not have these limitations.

• あなたが必要な場合 複合検索キー、複合検索キーを使用して検索データ セットに列を追加し (つまり、区切り文字として「#」などを使用してさまざまな属性を連結します)、VLOOKUP/XLOOKUP を使用してこの新しい列を検索します。

• 一部 注意点 VLOOKUP を使用する場合:

  • 完全一致を保証するために、関数 VLOOKUP の最後の引数として「false」を追加することを忘れないでください。

  • 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.

  • Excel では 100.000 行を超えるデータ セットを使用しないでください。 データ セットが大きくなると、Excel のパフォーマンスと安定性に問題が生じます。
    大規模なデータ セットで VLOOKUP を使用する場合は、計算モードを「手動」に設定すると興味深い場合があります。そうしないと、データに小さな変更を加えるたびに Excel がすべての VLOOKUP を再計算します。

  • VLOOKUP は XNUMX 番目の引数として返す列番号を持ちます。 この数値は列を追加または削除するときに動的に調整されないため、列を追加または削除するときに必ず調整してください。

  • 一致するだけの場合は、式「=IF(ISERROR(VLOOKUP( 、 ,1,false),”一致しません”,”一致”)”

これらのトリックは次のことに役立ちます 手動マッチングを高速化しますしかし、明らかに実際の自動化は常に優れています。

金融サービスにおけるマッチングは、 多面的な挑戦, 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.

私のすべてのブログをチェックしてください https://bankloch.blogspot.com/

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