The financial services industry has evolved over time. Some of the main characteristics of this evolution are:

  1. The complexity of the financial transactions: financial transactions have become more complex ranging from the number of counterparties involved to the hybrid nature of the transactions. A single transaction may involve numerous counterparties to bid, offer, trade or even settle. Also, these transactions may be “derived” from other transactions hence the term “derivatives”; they are hybrid transactions. Various methods would then be required to value these transactions accurately.
  2. The volume of financial data: with the advent of technology, mergers and acquisitions, there has emerged mega-banks. These banks are characterized by millions of customers and transactions. Hence the quantum of data generated by these transactions are much more substantial than it used to be.

iii.            The complexity of banking regulation: Due to the series of banking crises recorded round the world over the years, the Basel Committee on Banking Supervision (BCBS) has been designing and amending a universal framework for banking regulation. Banking regulation has now become a global phenomenon beyond national jurisdictions. It is expected that any financial services industry of repute must adopt these Basel standards as best practice.

My experience from a couple of years as a consultant in the Nigerian financial services industry has given me insight into the enormity of challenges encountered in data intensive projects.

Due to nascent developments around the world, it is apparent that every financial institution must have a global outlook. One of the aspects which must have this outlook is reporting and disclosure requirements. This sort of outlook ensures that compliance with some predetermined international standards is inevitable. It is expected that any financial services industry of repute must adopt these standards as best practice. There are almost as many standard reporting requirements as there are stakeholders (customers, investors, regulators, investors etc.). Some of these standards to mention but a few are:

  1. Basel II, II.5, III
  2. IFRS
  3. Compliance
  4. Risk
  5. Performance Management

The data requirements for each of the examples above are in most cases a daunting task to meet for the following reasons:

  1. The complexity of the financial transactions: financial transactions have become more complex ranging from the number of counterparties involved to the hybrid nature of the transactions. A single transaction may involve numerous counterparties to bid, offer, trade or even settle. Also, these transactions may be “derived” from other transactions hence the term “derivatives”; they are hybrid transactions. Various methods would then be required to value these transactions accurately. These methods tend to have numerous dependencies hence complex business logic and rules need to be applied in their implementation.
  2. The volume of financial data: with the advent of technology, mergers and acquisitions, there has emerged mega-banks. These banks are characterized by millions of customers and transactions. Hence the quantum of data generated by these transactions are much more substantial than it used to be. The collation and presentation of this data in a systematic manner poses a big challenge.

More than ever, the data requirements for Basel, IFRS (especially IAS 39 & IFRS 9) and Risk have become intertwined. Gone is the era of analyzing data in silos. For example, most IFRS disclosure requirements are dependent on Risk data.

Many financial institutions in Nigeria have spent a fortune on procuring software and applications that intend to meet most of these reporting requirements. However, the results in most cases are failed projects. Why? The inability to meet the complex data requirements! Also, the inability for those systems to accommodate the business logic of the data involved.

In order to avert huge wastage in capital outlay by procuring applications that are a misfit for business requirements, the financial institution would have to go back to the drawing board to plan their data. I will call this a “Financial Data Model Strategy”.

A Financial Data Model Strategy is a holistic plan mapped out by an organization to determine the structure and correlation of its financial data in a systematic manner. It is a long term plan in which all the data requirements for an organization’s financial reporting are determined and designed into a model.

Since this strategy is scalable, all future additional data requirements would fit into the model. Some of the data to be taken into consideration in this plan are:

  • Static Data: Customer, Profit Centre, Account Officer, Currency etc.
  • Market Data: Interest Rates, Market Rates, Market Prices, Yields, Exchange Rates etc.
  • Contract Data: Deal reference number, contractual information etc.
  • Valuation Data: Effective Yield, Mark-to-Market, Fair Value, Value-at-Risk, etc.

With this model, the financial institution should be able to conveniently meet the data requirements for any software implementation. The model is a document showing the type, structure, nomenclature and interrelation for all financial data. The document should contain both business/functional and technical sections.

Going back to the basics means painstakingly building a financial data model that is robust enough to meet all your complex reporting requirements – IFRS, Basel, Risk, Compliance, and Performance Management (GRC).

The solution does not begin with procuring an expensive application. It begins with planning your data systematically. In doing this you save so much money from failed MIS projects.

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