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Managing Reference Data risk – A best practice approach

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Derek-Kemp

By Derek Kemp, EVP and Head of EMEA Region, iGATE
With an array of new regulations and a challenging economic environment, financial institutions are under immense pressure to improve data management processes and mitigate risk. The poor quality of data,substantial redundancy and duplication of effort in the area of reference data management, continues to create major problems for financial institutions, globally.Derek-Kemp

As a first step, firms first need to understand the gap that exists between their current reference data management processes and newer best-practice approaches.

The business impact of poor data quality

In the rush to improve efficiency with straight-through processing (STP) have firms failed to pay sufficient attention to the risks associated with poor data quality?

Based on current market trends, this certainly seems to be the case.

Duplication of reference data results in unnecessary costs: All large financial firms access data from a variety of sources where disparate and siloed data systems and operations are the norm. To add to the complexity, organizations typically source reference data from a range of internal and external providers based on the data consumption requirements of different departments. This siloed style of functioning however, makes it difficult for an individual department to access data that may have already been purchased by another department. This typically leads to reference data purchases being duplicated, thereby leading to unnecessary costs.

Increased operational risk: When inconsistencies or inaccuracies in reference data arise, exceptions in the trade lifecycle occur, leading to increased operational risk, lost revenue opportunities, and financial liabilities. This gains even more significance during periods of market volatility when firms feel the need for expensive and time-consuming manual trade duplication and reconciliation processes.

Consolidating market data management and counterparty systems

The reference data management problem is shared by both the buy-side and the sell-side. Each has to worry about securities reference data to settle trades reliably and to provide fair valuations. The sell-side firms are now beginning to consider market data and counterparty data as part of the larger reference data problem.

Trading firms have for a long time been concerned with sourcing accurate market data on a real-time basis with minimum latency in order to feed algorithmic trading engines. Only the largest Tier 1 firms can afford the luxury of storing their own tick histories, but for the majority, there are cost-effective industry utility services such as Tickdata and Reuters DataScope Tick History, which provide clean tick history on demand. These can be used for proof of best execution and algorithmic back testing.

Firms should closely examine whether the additional costs of the more complex platforms and the cost of the actual tick data storage provide sufficient benefits when compared with less expensive securities reference data management platforms and tick data history utilities.

Many firms have invested heavily in counterparty data management. However, through a spate of recent acquisitions, organizational, geographic and functional silos have developed resulting in multiple databases in different formats. For a firm, there is undoubtedly a benefit from integrating counterparty data in one place. Now that industry utilities exist where clean data can be reliably sourced, it is no longer a proprietary advantage. The advantage would stem from the uniform usage of that data source throughout the firm.

Market data and counterparty data should be acquired as a utility and mapped to reference data through cross-referencing. But a robust reference data management platform must underpin such efforts if they are to succeed.

Managing multiple risk points for efficient reference data management

Best-practice reference data management may involve outsourcing, but only when certain risk points in the process have been considered:

Risk-point 1: Purchase: In the case of reference data, it is common to buy multiple sets from multiple vendors, according to the needs and preferences of individual employees and teams within the organization. This results in organizations spending money on duplicate, non-optimized data. Best-practice reference data management circumvents purchase point risks by using advanced tools to track and analyze what data sets are being purchased and where they are being used, tracking the data path from contributor to consumer, and then monitoring by user and data element. Smart companies also turn to trusted independent partners to help them determine what data sources are right for the organization and its employees.

Risk-point 2: Cleansing: Reference data generally is non-optimized at the time of purchase and hence it needs to be cleansed in order to identify inconsistencies and faults. If reference data is held in multiple silos rather than centrally, it is likely to be cleansed multiple times. There is also the increasing impact of corporate actions on reference data to consider. Some corporate actions are relatively easy to handle, such as splits and dividends. Others, like complex rights issues, require labor-intensive intervention from a specialist. As a best-practice for reference data management, use automated tools that cleanse data by monitoring incoming data and checking for relationships and expected patterns. When exceptions occur, manual intervention may be required but smart companies use skilled staff in low-cost offshore locations to do this.

Risk-point 3: Distribution: Once reference data has been bought and cleansed, it needs to be fed to the individual systems that consume it. That requires an in-depth understanding of what data sets and fields each consuming system requires, when the data is needed, and in what format it is expected. As a best-practice for reference data management, use ETL (extraction, transformation, and loading) tools to subset and reformat the data contained in the Golden Copy, and send it to each consuming system in a way that it can be understood and used. When a request for a new feed is submitted, skilled personnel are on hand to decide which fields from which data sets are required to build it.

Leveraging third-party specialist organizations to manage commoditized reference data

Many firms have now realized that there is little competitive advantage to be gained from managing publicly available, highly commoditized reference data in-house. Increasingly, firms are turning to third-party specialist organizations, not only to manage reference data on their behalf, but also to re-architect data systems in such a way that they are outsourcing ready.

Using a combination of best-of-breed tools and skilled resources, the third-party specialist will normalize, cleanse, validate, and aggregate multi-source content to achieve a single, standardized Golden Copy of the reference data, which is fed back to the client as a managed service. There has never been a better time for financial firms to leverage third-party specialist organizations to manage commoditized reference data. The risks associated with reference data now are an immediate concern and require immediate action. Best-practice reference data management is critical to current performance and a prerequisite for achieving further growth and efficiency.

 

 

 

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The case for AI technology adoption in financial back-office roles to improve efficiency

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The case for AI technology adoption in financial back-office roles to improve efficiency 1

By Tomas Gogar, AI CEO, Rossum

In this era, digital transformation isn’t anything new. Nonetheless, it can still cause a lot of confusion and resistance for some companies, many of which are often slow, unwilling or unable to implement the necessary changes to embrace technology. As a result, entire industries are barely scratching the surface when it comes to shifting to the digital world, and many, from the insurance industry to logistics and delivery are still catching up on the digital transformation.

The banking and financial sector have been notoriously slow in adapting to the online world. They paid the high price for it, giving way to a flurry of incredibly successful new disruptive players, built on cutting edge tech from the ground up. From Transferwise, Revolut or Venmo, to GoCardless, this new generation of fintech companies addressed consumers changing expectations in a way that traditional retails banks simply couldn’t.

To catch up, incumbent players have prioritised the user interfaces, giving the appearance of a digital offering, and oftentimes leaving the back end infrastructure untouched, and hence the processing power, accuracy and speed unaffected. Back-office functions, although they are essential to the smooth running of a business, have seen very little change and as a result,  too many people in these functions are still tied up typing information into spreadsheets and software forms – in fact, manual data entry is a prime example of how much resources the offline legacy wastes. Take Accounts Payable for example, invoice data entry in this sector is estimated to eat up roughly 100 human lives worth of time every single day.

Tomas Gogar

Tomas Gogar

With the significant increase in the number of employees working from home due to the global COVID-19 pandemic, the back-office challenges have suddenly come to light, and finally, companies that got away with minimal changes so far, are realising that they need a structural digital overhaul, and fast. We believe the solution to this is artificial intelligence backed software solutions.

Previous technology based solutions essentially did half the job, heavily depending on human fact checking. Consequently, these solutions were actually quite cumbersome and time consuming and costly to implement and maintain, and offered only incremental improvements. Now with AI, automises data processing completely removing the need for human fact checking (and human error!). Additionally, deployment is massively simplified with an average setup time of one week, compared to about 6 months for previous technologies.   AI solutions are also highly adaptable to new formats and scenarios, allowing businesses to test them in say one department and to quickly roll out a single unified solution across all functions of the business.   Data can be extracted from any invoice layout with no template or rule set-up, saving significant and effort. Rather than trying to change and standardise a highly fragmented environment (there are about as many invoice formats as there are businesses), AI can work with it, and optimise the overall process and offer a unified answer to a fragmented ecosystem.

Taking Accounts Payable as an example again, this is a sector that has relied by and large on Optical Character Recognition (OCR) software solutions in an attempt to remove some of the manual labour involved in reading processing and filing invoices. Although OCR did improve the processes to a certain degree, ultimately these types of solutions still required a long and expensive set up processes and a lot of manual labour to actually capture the data accurately with templates and manual data entry. Now, with AI software, like the one we have created, this is a solution that makes data extraction simple and easy, saving time and man power, as well as building on existing infrastructure. It has the ability to transform this industry.

In conclusion, for a sector that has been slow to adopt digital change, AI is THE technology answer that is finally fixing the invisible pain points that businesses had simply accepted as unremovable. AI applied in this way offers a viable way forward and businesses that were notoriously slow and resistant to embrace the digital transition, incentivised to make a change, may actually end up at the head of the pack. Skipping ‘older tech’ and jumping straight into AI solutions, the best scenario available by far, is indeed the smartest, fastest and most cost effective way to transition into the digital world.

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InsurTech is helping to drive the digital evolution of the UK motor retail industry

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InsurTech is helping to drive the digital evolution of the UK motor retail industry 2

By Alan Inskip, Tempcover CEO & Founder

If the last nine months have made anything clear, it is that the pandemic has fundamentally changed both buying and driving habits for UK motorists. The latest Tempcover research has revealed that online-only used car sales had increased fifteen-fold during the pandemic among 2,000 survey respondents.

Before lockdown, just 4% of used car sales were fully-digital. The vast majority of those surveyed opted for either a physical purchase (50%) or a digitally-assisted purchase (45%), relying on a combination of digital tools and an in person viewing or road test before buying.

While car sales overall are down on last year’s figures*, one in six (17%) of those surveyed had bought a used car during lockdown, with two thirds (64%) relying on a fully-digital purchase journey. Digitally-assisted purchases counted for one in five (20%) used car sales, while in person sales fell to just 15% – no surprise considering the ongoing social distancing measures.

And when it comes to arranging insurance for their recently-purchased vehicle, our survey participants displayed an equal balance between telephone and online as the preferred method (48% each). Nearly a third of those (28%) said they wait up to ten minutes for their policy to be confirmed, and a further 22% wait as long as 20 minutes to get cover.

The switch to digital insurance, driven by InsurTech

In the midst of rapid and significant market changes, many traditional insurers have lacked the agility and flexibility to adapt accordingly. InsurTech can provide immense value in bridging that gap, as the digital solutions are entirely scalable, with the flexibility to substantially increase in size and across multiple geographies, with minimal disruption.

Alan Inskip

Alan Inskip

The ongoing decline of physical transactions in the motor retail industry is a perfect example of how InsurTech is adding value. Several national blue-chip dealerships, with both physical and digital showroom floors, are already streamlining their online purchase process by offering temporary driveaway insurance policies to cover the vehicle for a fixed-term, usually between five to seven days, as part of the purchase journey.

The entirely online one-step user experience is the first of its kind in the traditionally outdated and inflexible driveaway insurance industry and it is dramatically simplifying the process of how insurance is purchased and consumed. Due to the flexibility and agility of the digital solution, each retailer has its own unique URL, where the customer can obtain a simple single-cost policy in just 90 seconds through an entirely digital process, which fits in line with the evolving consumer purchase trends.

For the dealers, this technology means more efficient stock clearance times and greater profitability. For the buyers, it takes the stress out of searching for annual insurance on the spot, and provides the driver with near instant cover so that they can immediately drive their new car, while giving them the opportunity to thoroughly research the best annual policy to suit their needs. An added benefit is there’s no risk to any existing No Claims Discount, as it’s a separate and standalone policy.

While there is a chance these trends will reverse to some extent post pandemic, it is clear that the consumer appetite for digital purchase and consumption is here to stay, and InsurTech will continue to lead the way in making motor insurance more easily-accessible across digital platforms, while offering consumers the best value for money.

* https://www.thisismoney.co.uk/money/cars/article-8615851/Used-car-sales-halved-lockdown-brakes-1m-motor-transactions.html

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Five ways enterprises are using the public cloud

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Five ways enterprises are using the public cloud 3

By Michael Chalmers, MD EMEA at Contino

The public cloud is the most significant enabler in a generation. It’s causing a massive shift in how businesses are operating and tearing apart previous business models.

Amid challenging economic times, it’s inevitable that spending within IT is dropping. However, the cloud is the only segment that is still growing. The public cloud is increasingly becoming a central element of enterprise IT.

Contino asked 250 IT decision-makers at enterprise companies across Europe, USA and APAC within companies of over 5,000 employees about their views on the state of the public cloud within their organisation at the beginning of 2020.  Nearly all of them (99%) saw a significant technical benefit compared with on-premises.

Here are some other ways public cloud is being used by enterprises:

  1. Widely, albeit not yet business wide.

A whopping 77% of enterprises are using the public cloud in some capacity. Overall, 50% of businesses are utilising a hybrid cloud, 22% single private cloud, 20% multi-cloud, 7% single public cloud and only 1% are using only on-premises.

But only 13% of businesses have a fully-fledged public cloud program. The largest set of respondents (42%) have multiple apps/projects deployed in the cloud. 24% were still working on initial proofs-of-concept, and 18% were in the planning stages.

83% of respondents said they want to grow their cloud program. Almost half (48%) do wish to grow, but with caution, while 36% want to move as quickly as possible.

Only 4% plan to revert to on-premises but are in no rush to do so.

  1. To enhance security and compliance versus on-premises, although these are still also seen as barriers to adoption.

A massive 64% of respondents stated they find this more secure than on-premises, and only 7% see it to be less secure. 72% found it easier to stay compliant with business data in the cloud versus only 4% who found it harder.  However, 48% cited that their biggest barrier for not using the cloud was security, and 37% stated the need to remain compliant was the most prevalent blocker.

Other challenges also posed a barrier: a lack of skills, the cost to purchase and cloud-native operating models not working with existing investments made up 29-32% of responses.

19% stated that lack of leadership buy-in is the biggest barrier, reflecting that a significant number of IT departments have a need for this solution but have not been provided with the support to do so. However, relatively speaking, this was one of the least-cited barriers.

  1. For improved efficiency, scalability and agility, but vendor lock-in is still a major concern.

The top three cited technical benefits of public cloud were better efficiency, agility and scalability versus on-premises. However, 63% of IT professionals were ‘somewhat’ or ‘very much’ afraid of the commitment that can come with investing in the cloud. This is another major barrier that is preventing businesses from ​migrating to the cloud.

Only 23% are not afraid of being locked in and a meagre 5% have no fear at all. However, the fact that 77% of businesses are using the cloud shows any risk of being locked in is outweighed by the benefits of the cloud.

  1. To align IT with the business.

This is by far the most cited business benefit of the public cloud. 100% of those surveyed witnessed varied business benefits versus on-premises. Other major benefits include the ability to focus on new revenues (43%), accelerated time-to-market (43%), and increased ROI (40%).

  1. To accelerate innovation and increases cost-effectiveness.

Innovating in the cloud was quicker for 81% of respondents. What’s more, not one person surveyed said the cloud slowed down their innovation. 79% have saved money with the cloud and only 5% have found it more of an expense than on-premises.

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