Nevidna hrbtenica bančništva: globok potop v usklajevanje in usklajevanje

Nevidna hrbtenica bančništva: globok potop v usklajevanje in usklajevanje

Časovni žig: 1. december 2023 5:34
Izvorno vozlišče: 2988681

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 ročno kodiranje, ponavljajoče se naloge in velika odvisnost od Excela.

Posebej ročni in hkrati avtomatiziran postopek v sektorju finančnih storitev je ujemanje in sprava. 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.

Kljub razpoložljivosti dovršena programska oprema (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 večina ujemajočih nalog se pogosto opira na prilagojene ali ročne rešitve, 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…​

Za razumevanje je potrebna boljša sprava seciranje njegovih sestavnih delov, tj

  • Začne se z zbiranje in preoblikovanje različnih nizov podatkov za primerljivost. 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.

  • Naslednji korak je opredelitev a natančen algoritem ujemanja. 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.

  • Ko je algoritem ujemanja definiran, vnesemo fazo primerjave. 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.

  • Končno, ugotovljena neskladja je treba pretvoriti v ukrepljive rezultate, kot so poročila, sporočila sodelavcem ali tretjim osebam ali korektivni ukrepi (npr. ustvarjanje datotek, sporočil ali stavkov SQL za odpravo razlik).

Zapletenost ujemanja v finančnih storitvah je raznolika. Naj raziščemo nekaj tipičnih primerov uporabe na področju finančnih storitev:

  • Večina bank ima a Glavna datoteka vrednostnih papirjev, 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).

  • Pri poslovanju s prebivalstvom je očitno nujno enolično identificirajo in ujemajo z določeno fizično osebo. 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…​

  • Zelo podoben problem je ujemanje s podjetjem ali natančneje s trgovino. 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.

  • Drug zanimiv primer je ujemanje avtorizacije in klirinškega sporočila pri plačilu s kartico VISA. 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.
    Ta vrsta ujemanja velja tudi za druge primere uporabe plačil, kot je npr. ujemanje dokončane predavtorizacije s predhodno predavtorizacijo ali vračilo kupnine s predhodnim nakupom.

  • Primer finančne uporabe, ki zadeva skoraj vsako podjetje, je ujemanje računa in plačila. 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.

Kot lahko vidite, ujemanje še zdaleč ni enostavno, a razumevanje osnovnih korakov lahko pomaga pri boljšem ujemanju. Medtem Excel kljub svojim omejitvam ostaja močno orodje za (ročno) ujemanje. Zato a quick reminder for everyone who wants
to do matching in Excel:

  • Uporaba VLOOKUP za izvedbo ujemanja. Vendar ima VLOOKUP določene omejitve, kot je dejstvo, da prikaže napako, če ni ujemanja, in da lahko iščete samo v prvem stolpcu. Močna alternativa je uporaba XLOOKUP, Ki je
    does not have these limitations.

  • Če potrebujete sestavljen iskalni ključ, dodajte stolpec v nabor iskalnih podatkov s sestavljenim iskalnim ključem (tj. združite različne atribute z npr. »#« kot ločilo) in nato uporabite VLOOKUP/XLOOKUP za iskanje v tem novem stolpcu.

  • nekateri točke pozornosti pri uporabi VLOOKUP:

    • Ne pozabite dodati »false« kot zadnji argument funkcije VLOOKUP, da zagotovite natančno ujemanje.

    • 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.

    • V Excelu ne uporabljajte nizov podatkov z več kot 100.000 vrsticami. Večji nabori podatkov so problematični za delovanje in stabilnost Excela.
      Prav tako je lahko zanimivo nastaviti način izračuna na »Ročno«, če delate z VLOOKUP na velikih naborih podatkov, sicer bo Excel znova izračunal vse VLOOKUP vsakič, ko naredite manjšo spremembo podatkov.

    • VLOOKUP ima številko stolpca, ki jo vrne kot tretji argument. Ta številka ni dinamično prilagojena pri dodajanju ali odstranjevanju stolpcev, zato ne pozabite prilagoditi pri dodajanju ali odstranjevanju stolpcev.

    • Če želite le ujemanje, lahko uporabite formulo »=IF(ISERROR(VLOOKUP( , ,1,false),"NI UJEMANJA","UJEMANJE")"

Ti triki lahko pomagajo pri pospešite ročno ujemanje, a očitno je prava avtomatizacija vedno boljša.

Ujemanje v finančnih storitvah je a večplasten izziv, 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.

Oglejte si vse moje bloge https://bankloch.blogspot.com/

Časovni žig:

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