Business knowledge, data centralisation and data consistency are at the heart of successful data management for finance, risk and regulation, particularly as it applies to the complexity of financial institutions operating across multiple regulatory environments. The challenges to bringing these elements together are significant, but they can be tackled and results can include not only accurate and consistent regulatory reporting, but also potential for product innovation.
These issues and more were debated during A-Team’s latest Hot Topic webinar, sponsored by Wolters Kluwer Financial Services, provider of the Summix finance, risk, compliance and performance platform, and moderated by Andrew Delaney, editor-in-chief at A-Team.
Delaney set the scene for discussion, describing the challenges of data management including the growing complexity of financial institutions’ trading and investment activities, an increasing tendency for firms to operate in multiple regulatory environments, and the difficulty among firms operating across borders of meeting multi-speed regulatory requirements. Alighting on one overriding issue running through these challenges, he noted the difficulty of maintaining data consistency at group and operating levels – an imperative for finance, risk and regulatory compliance.
Taking on this issue, Selwyn Blair-Ford, head of global regulatory policy at Wolters Kluwer Financial Services, said: “Data consistency is important to understanding the business and its risks. To understand the business, it is necessary to have a consistent data foundation and order. The data structure may not be good enough to respond to regulators’ expectations that data from different parts of the business can be correlated and compared immediately, but firms can develop an approach that works towards these expectations.
“On a positive note, data consistency can drive innovation. It can provide cost efficiencies and help detect new trends that could be packaged into new business opportunities and added to the bottom line.”
Owen Burke, head of finance at Elavon Financial Services, a bank based in Ireland with branches across Europe and a long-standing user relationship with Wolters Kluwer Financial Services, concurred, saying: “Our need for data consistency is to stop regulators coming down on us and to drive capital efficiency. Regulators are interested in correct data. If data is not correct, they make enforcements, fine firms and, worst of all, firms suffer reputational damage.” On a more positive note, he added: “Departments in large banks can use consistent data to cross-sell products. It is evident that there is capacity to build and sell products, and show revenue benefits to senior management.”
Regulations exerting particular pressure on data management and requiring a consistent data infrastructure were named by webinar panellists as Basel III in Europe and Dodd-Frank in the US. Blair-Ford commented: “Regulators may look at three or four reports and find discrepancies. They ask questions. It is only possible to respond if data is well structured. Irrespective of business line, the same reports are required by regulators.”
This requirement can be taxing as data must be broken down to deliver consistent finance, risk and regulatory compliance reports. Burke said Elavon first implemented reporting for finance and then moved on to reporting for risk and regulatory compliance. He commented: “I own this. We sign off what we produce as 100% correct, which means business lines have correct and consistent data.”
Expanding on the issue, Blair-Ford stressed the need for data consistency and auditability despite the different requirements of the data in different parts of the business, and the tendency of finance to look backwards in terms of recording and using yesterday’s data, while risk looks forward and is also concerned about the here and now. Similar, but different, he added treasury liquidity, which relates to risk but needs another view of the data in different timeframes and at different frequencies.
He said: “These functions are all part of the same activity, but they all need a different reflection of the data to do their jobs effectively. This is complex from a data management perspective.”
Acknowledging the complexity of data management across finance, risk and regulation, Delaney pushed the question of what can be done to find a solution to complexity back to Blair-Ford. He responded: “The main obstacles are around who cares. Historically, one or two areas have competed for control of a company, perhaps finance controlling it first and then risk, or credit managers. Each of these areas has a particular way of looking at the business and this can create data silos. This isn’t good enough. All views must be combined to answer business questions properly. The need is for a unified structure and to look at what is common and what is different between departments. There has to be someone with a broad vision to see the requirements of all and to pull out commonalities and differences.”
Burke added: “We built data up six or seven years ago as the bank built up. We need to find owners of data in the business for governance purposes, but no-one wants to own the data, which is a challenge.”
On the technicalities of developing data management for finance, risk and compliance, Delaney turned to Vlad Botos, global product manager at Wolters Kluwer Financial Services. He explained: “A lot of firms are looking at a centralised approach, sometimes taking a solution from one of their country operations and trying to expand and mould it to meet the requirements of several regimes. This requires expertise and knowledge of local regulation. For example, there can be complications around different definitions used in different regulations but with a similar context. The aim must be to limit duplication of data content.
There can also be difficulties where regulation prevents data crossing borders. This makes a central data repository more difficult to manage, but it can be done.”
Taking a top-down view, Blair-Ford advised that any approach must be led by business knowledge and with an understanding of why data is being collected and what will be done with it. He pointed to the need to implement a data architecture that is flexible enough to provide data in the correct context to consumers in the finance, risk and compliance functions, as well as the need for data architecture that can both aggregate data for group level users and provide drill down capabilities. He concluded: “The key is to remember that the outcome of any development must be a good reporting system that gets the right information to the right people at the right time.”
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