The relationship between consumers and their banks is particularly complex; it can be one of love and hate. When it comes to matters of money, consumers have traditionally been unlikely to rock the boat and whilst many complain, few change. Unsurprisingly, banking gained a reputation as a closed industry, with the established players holding dominance, while start-ups struggled to gain the momentum to challenge.
As the world enters a digital age, these days are over. The digital-only challenger banks, such as Starling, Monzo and Revolut, have exploded and targeted the millennials. Following the lead of these trailblazers, the established banking industry has invested heavily in developing their own digital capabilities, with the mobile channel now overwhelmingly the de facto method of money management. HSBC, for example, noted that more than 90% of its customer interactions came via digital channels, up from 80% in 20161. Some banks have even gone as far as setting up an entirely new bank, rather than untangling the legacy debt they face.
Digital and challenger banks can often start from different places when they decide to enter the banking market. However, they ultimately all share a common requirement to deal with a complex and wide-ranging regulatory regime and must prepare and submit supervisory data to the national regulator in a range of formats.
The Regulatory Environment is incredibly complex with 58,000 financial services firms affected in the UK2, which in turn, increases the complexity of Regulatory Reporting. This means banks, including challengers entering the market, will need to fulfil increasing regulatory obligations on a regular basis. EU banks are currently committed to reporting upwards of 700,000 data points quarterly, in layers upon layers of different templates. This is just for prudential reporting; combined with statistical reporting, it is much more. Unlike the established banks, which have huge legal and compliance teams, the challengers are much more thinly resourced. According to the Global Banking and Finance Review, the financial services industry, spends close to $70 billion each year on risk, risk data and regulatory reporting3. Given that challenger banks are often small businesses themselves, they are likely to struggle with accessing the adequate funding and expertise to enhance their regulatory status to meet the rigorous rules.
Another difficulty that firms face is the frequency with which the reporting framework rules change. The consequence is a substantial degree of effort to ensure continued adherence to the rules, requiring some firms to overhaul data, systems and underlying IT architecture to remain compliant. For instance, the EBA regulatory reporting framework has already changed nine times in the last six years, introducing various amendments to COREP and FINREP requirements. This has led some financial commentators to argue that there is something inherently flawed within the regulatory system and it is clear there is much work involved in order to alleviate such a burden.
However, whilst new entrants to the market face significant challenges, they can utilize technology to avoid the mistakes of their predecessors. They are in the prime position to get it right the first-time around avoiding the major legacy debt suffered by the traditional banks. Henceforth, avoiding the prospect of having to decide whether to untangle years upon years of legacy debt or simply starting again from scratch.
When Common Reporting (COREP) was implemented in 2014, it was deemed as a huge triumph with the intention of standardizing the reporting of capital requirements and prudential regulatory information across the EU. The belief behind COREP was that if all regulated credit institutions and investment firms were on a harmonized level of reporting, the regulators would be able to draw comparisons from institutions across the EU. However, with highly aggregated data at the output level, regulators were, and are, often unable to make any significant comparisons. If the data quality is poor on an input level, with no standardization, then the resultant data is also poor, creating significant problems for the regulators. Challenger banks must consider these aspects in their quest, and it is precisely here that a granular data model can assist challenger banks in getting their data management and regulatory reporting correct the first time around.
By using an automated highly granular one-to-many data model solution for End-to-End regulatory data production, and a consistent input data model with a direct lineage to the appropriate output, challenger banks can cut through this major obstacle which continues to plague the established players in the industry.
The results of this can be seen with the introduction of PRA110 in the United Kingdom in July 2019. Firms faced the introduction of a new liquidity report with nearly 260 rows by 112 columns totaling up to 29,000 data points. This extreme granularity was an utmost challenge to firms.
However, with a one-to-many data model solution, such as Abacus360 Banking software solution, firms only had to provide a fraction of those data points, as much of the data already existed within the model for LCR & ALMM Liquidity Reporting.
The benefits of a granular one-to-many data model can also be highlighted by financial institutions who are faced with major regulatory reform, such as the upcoming CRRII/CRDV regulation. This major regulatory change will not require a major overhaul of data, as the basis of many of the calculations and data points already exist within the current data model. Therefore, ensuring that challenger banks can avoid the mistakes of their predecessors.
In conclusion, the regulatory environment is highly complex and continuously evolving. In order to succeed, challenger banks must understand the regulatory landscape and position themselves for growth, avoiding crippling legacy debt. Above all, we believe that challengers must look to shift the dial on the level of standardization from the output level to the input level and we know that a highly granular one-to-many data model solution can provide the answer.