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COMING OF AGE: BIG DATA AND FINANCIAL SERVICES

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COMING OF AGE: BIG DATA AND FINANCIAL SERVICES

Peter Pop, SVP Financial Services, HCL Technologies

Big Data was the phrase on the lips of many business leaders last year, with the concept already moving past the Peak of Inflated Expectations in Gartner’s Hype Cycle. The current Big Data landscape is one of increasing maturity. We are seeing some benefits being accrued by early adopters, while others look to lay firm foundations by reigning in control of their data sprawl to create a central data lake, opening up access and insight.

Insurance leads the way

In the financial services sector, insurers have been leading the way when it comes to Big Data. Many of them are already using it throughout their businesses, from making sure the appropriate product is offered to customers, mitigating risk through predictive modelling and reserving through to identifying potentially fraudulent claims.

Many insurers are also becoming increasingly innovative when it comes to capturing and using customer data. For example, Aviva’s in-car app monitors the driving style of its customers, offering a 20% discount on car insurance for careful drivers. While in contrast, the banking sector has been much slower to get on board with Big Data, we are now increasingly seeing banks embark on a Big Data journey to support their back-office operations.

To a large extent insurers are seeing Big Data as a means to overcoming their historic problems with customer visibility and then using this improved customer transparency to drive their business forward without the need for lengthy MDM initiatives or significant legacy transformation.

What’s the driver?

There are three main drivers for banks to adopt analytics of customer data: customer retention, increasing market share and capturing a larger share of the customer’s wallet.  By using customer data, banks can analyse the probability that a customer will take its business to a competitor.  Early intervention can reduce this loss. There are success stories where banks have been able to significantly increase customer acquisition through careful analysis of existing customer behaviour, from both on-line and off-line sources. This data is then used to feed into a CRM solution, giving the call centre more targeted leads.  The data can be fed back into the teams building the customer touch points like mobile and web applications to improve the customer experience and personalisation when using these channels. Customer data analytics has been successfully used to target customers with products that were more relevant to their individual requirements. The take up rate from these targeted campaigns is significantly higher than the typical scatter gun approach used without applying data analytics.

Barriers to adoption

By far the most significant reason for the slow uptake of Big Data projects in banks is the siloed nature of data held in their existing legacy systems and data repositories. This often means that data is not shared across the enterprise, and when it is, it often only covers some areas; limiting the field of vision and making it difficult to gain a 360-degree customer view. A lack of internal skillsets is also proving to be a challenge for financial services firms. Data management is a brand new skillset beyond what already exists in the sector, with expertise required in statistics, mathematics and programming to make it a success. On the other side of the coin, people must also be trained to interpret this data, or it is pointless producing it in the first place. As a new discipline, it is hardly surprising that there is a significant shortage of people with the right skillsets to be able to successfully implement Big Data projects and drive benefits for the organisation.

Another thing to take into consideration is the fact that a lot of time is required to analyse large amounts of data. It also requires huge amounts of processing power and storage, which cost significant amounts of money, meaning Big Data must be a priority within a business in order for it to be a success. At the start the cost of Big Data projects outweighed the benefits, but now this isn’t the case as the technology and its application has matured. Of course, there remains the potential risk that customer data could be breached as a result of Big Data analytics activity. For example, if sensitive data sets that have previously sat in isolated, high-security siloes are transferred into less secure databases for cross-referencing with other, less sensitive data at rest in a data lake, this could inadvertently lead to it being more vulnerable to a data breach. As a result, financial services firms will need to pay careful attention to the data they are using in analytics projects and ensure it has the appropriate security measures in place to protect it at all stages.

Battles being won

We are now seeing banks turning towards Big Data technologies in increasing numbers as many of these initial barriers are being overcome. Cost optimisation is now very much the name of the game; as such many banks are looking at the likes of Hadoop as their Big Data platform of choice, as it offers them greater flexibility at a significantly lower cost of other platforms on the market.

Big Data is also no longer the sole domain of data scientists, as the latest reporting and dashboard tools such as Tableau are putting digestible data directly into the hands of business users. The ability to present data to users in a way that anybody in the business can understand should not be underestimated, as it really starts to unlock the huge potential of Big Data within banking and to enable self-service Business Intelligence.

Wider drive for innovation

Change won’t happen overnight. Implementing and migrating data to new platforms takes time; a number of years as opposed to months. At first glance that could seem overwhelming, but bringing in the right support and expertise will lead to long-term benefits. Making an up-front investment of time will ensure that banks are set up to effectively manage and organise both existing and new data in the future.

Understandably, banks have so far concentrated their Big Data efforts on their back-office functions. However, there is a great opportunity for banks to drive deeper engagement with customers than they ever have before. A number of banks are already investing in innovation labs as they look to capitalise on the Big Data opportunity. Ultimately, banks must be able to walk before they can run – it is vital they take the time to lay the firm internal foundations so that the cost savings and improved customer experience will follow.

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Wireless Connectivity Lights the Path to Bank Branch Innovation

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Wireless Connectivity Lights the Path to Bank Branch Innovation 1

By Graham Brooks, Strategic Account Director, Cradlepoint EMEA

As consumers cautiously return to the UK high street in the past few weeks, banks can expect customer footfall in branch to rise accordingly. But whether it’s checking in for a mortgage appointment or cashing in a cheque, awareness of the ongoing potential health risks must be top of mind.

At the same time, the pandemic has forced a transition to the future bank branch. This means that there will be less people and more machines – digital signs, contactless devices, and new cash deposit systems.

To ensure they continue to provide a service that attracts new customers, banks must digitise their branches. And wireless technology is going to form the underlying infrastructure that makes that possible.

Wireless WAN providing reliability

Traditional banks now face their biggest challenge in history: digital-only banking. Over two-thirds of participants in a 2020 study planned to transition to a digital-only bank in the future. It’s therefore vital that traditional banks running physical branches update in-branch customer experience to compete with the new pack on the prairie. Reliability plays a big part. So does trust.

The future of in-branch experience lies in technologies such as IoT, VR/AR, and AI, all of which are highly data-intensive. Reliable connectivity is therefore critical, and banks should be shooting for zero-downtime connectivity, allowing no room for gaps in service.

To do this, banks can deploy Gigabit-class 4G LTE (LTE Advanced) or 5G adapters that bridge to a traditional ethernet connection, providing a wireless option to the wired-line router. Then, in the rare scenario where wireless connectivity is down, at least one of the WAN connections is always guaranteed to be live. The router has the autonomy to determine when failover is necessary.

Better still, the reliability of modern Gigabit 4G LTE and 5G connectivity now means that failover is often unnecessary. A branch can, therefore, run its network independent of a wired-line connection and benefit from the security and agility of a resilient wireless network, while still providing enterprise-grade connectivity.

Branch network reliability, in this way, will support the bank’s reliability as a whole. In turn, this will fuel the higher standards of customer experience needed to compete with more agile digital-only banks.

The new reality of IoT

The first organised response to stop the spread of the virus around the world was social distancing. While transparent screens can be used to block transmission, the overarching effect of these measures has been a loss of communication capabilities. This will affect banks like it has everywhere else, if not more as a space where interaction is so important.

IoT technology will be core to overcoming these barriers. Digital signage, kiosks, and surveillance cameras will all contribute to improved communication and security, and a better customer banking experience. But to enable such extensive use of IoT devices operating on a single network, banks must ensure they can accommodate such high levels of data transfer. Using Gigabit 4G LTE connectivity to extend its services beyond traditional network infrastructure, banks will achieve the required levels of bandwidth.

Hybrid connections managed in the cloud

With high volumes of data being transferred across the network, security and availability should be at the top of the agenda when digitising bank branches. But these are not always easy to implement, especially in an environment with several complex networks of endpoints.

Graham Brooks

Graham Brooks

For example, marketing teams need to push personalised content to customers on digital signs and IT teams need to set visitors up on a guest WiFi network. These operations require the guarantee of security and availability, with trust and the customer experience at the core.

Wireless networks excel in this aspect as they can employ the benefits of a cloud-based management system. Cloud-based systems make it easier for bank staff working from home, who can access the same assets and applications from their sofa as they would otherwise have in-branch. The service is the same.

Cloud management systems also provide improved network visibility, giving IT teams endpoint information from across the network as it happens. With security patches being updated on devices simultaneously, leaving reduced time for opportunistic attacks to exploit known vulnerabilities.

Equally, by using a hybrid Gigabit 4G LTE network in tandem with a wired connection, businesses can achieve simplicity from an otherwise complex challenge. The primary wired network can be used to transmit any sensitive information securely, while a separate network using the Gigabit 4G LTE connection runs other in-branch operations.

The branch’s network, in this way, is ‘air-gapped’. The secure data being processed by the operations team runs on an essentially separate network to that of the marketing team’s content. The network will also increase its ability to process more information, with its workload spread out.

The simplest solutions are often the best. In this case, exploiting a hybrid network can address the complexities of security and availability when employing enterprise-grade connectivity.

Invest now for future 5G rewards

As banks continue to adapt their branches over the course of the pandemic, they should invest in business-wide digitisation to secure a sustainable pathway to the future. To achieve this, banks must ensure their network solution enables carrier-class connectivity. It should make use of the full spectrum of connectivity – 4G LTE to 5G – and offer the full spectrum of 5G bandwidth. Branches aren’t going anywhere soon. They must ensure that their services are optimal now, and in ten years’ time.

Fortune favours the bold, and those who chose to adopt revolutionary and innovative technology early are already on their journey. Learning from this, banks that invest now to improve their future infrastructure will thrive once 5G does arrive. Good things do not come to those who ‘wait’. They come to those who prepare well in advance.

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Financial Regulations: How do they impact your cloud strategy?

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Financial Regulations: How do they impact your cloud strategy? 2

By Michael Chalmers, MD EMEA at Contino

How exactly do financial regulations affect your cloud strategy? It’s a question many of our customers have been scratching their heads about. Some solutions are costly and over-complex – but by asking the right questions, the wrong solutions can be avoided.

Following the Financial Conduct Authority’s (FCA) 2020 review, it’s clear that highly regulated enterprises must work harder than ever to stay within various limits which protect customers during an economically strenuous pandemic. Below, I address three questions we’re hearing from customers about how to optimise the cloud whilst sticking to FCA regulations.

  1. What regulations must you consider before outsourcing to a cloud provider?

If you have an application or workload that you’re looking to put into the cloud, you will have various service levels that you’ve defined for that particular stack. When you’re looking at the cloud provider and asking yourself what services to use, you’ll need to consider how that aligns to your service levels. How do I architect it to make sure that it’s aligned and that it can tolerate failure?

At the very start of that journey, before you even start putting your workloads into the cloud, you need to set the standards that you will need to adhere to. The Shared Responsibility Model is a key asset in understanding where your responsibility lies.

There are a number of things that you need to make sure are in your contract with the cloud provider. Unless you specifically sign a contract addendum with them, you can’t guarantee that useful and knowledgeable assistance is included.

While the guidelines are very clear on a number of clauses that you need to put in your contract with the cloud provider, these regulations apply to outsourcing in general. Cloud providers are very mature, so they will come with pre-packed addendums to the standard contract they offer that are customised to comply with FCA regulations. However, if you start outsourcing IT functions in a different way, e.g. if you start using a Software-as-a-Service (SaaS) provider which is delivered using the cloud, the new provider will need to be vetted to make sure that you have the right clauses in your contract with them. While cloud providers are very mature on this, most SaaS tools are not.

  1. How can you control or restrict where data in the cloud moves?

When it comes to data security, there are various options available on Amazon Web Services (AWS). For example, you can securely lock particular regions into an account on AWS. It’s also worth looking at your account structure policy. If you have accounts where data can’t reside outside the EU, you can put the workloads into that bundle and you can lock it down at policy level. There is an element of trust with the provider here as well.

While AWS offers prescribed controls to block certain regions, other cloud providers have different strategies. In the case of Google Cloud (GCP), you can specify service controls so that, even for managed services such as Big Query, you can lock your data in not just one region, but within your virtual private cloud. In other words, not only can you block specific regions or allow specific regions, you can specify that only things within a region can assess data within a region as a general policy.

  1. What does the regulator need to see to approve your exit strategy?

In terms of documentation, it’s not a case of “show me your policies and test plan” but rather “show me how you exercise it”.

Most of the time it’s a months-long process and it comes down to personal relationships: you build trust over time with the regulator as you build your exit plan. You should be able to discuss what else they would like to see in there.  While there used to be a template for an exit plan in the European Banking Authority (EBA) regulations in a previous version, this has since been removed.

Regulators don’t tend to look at test reports. However, they do post a lot of information on audit reports and auditors. These auditors are there to check you’re doing what you say you’re doing. At the end of the day you are responsible for demonstrating your exit plan – it has to be coherent, consistent and compelling.

Final Thoughts

The truth is, most of the time, regulators are just trying to encourage you to do what works. That being said, sometimes their outlook doesn’t quite match with your view, or sometimes there’s an artificial difference that can be smoothed over or finessed. Occasionally you have to remember that we had 2008. What if in 2020 we have a massive AWS outage?

Multi-cloud is a natural strategy. There’s a number of high-level, cloud-native services that can be leveraged to simplify the implementation of multi-cloud in large financial institutions. Adhering to the multitude of guidelines can be complex and time-consuming, but forging the right path through the regulations will ensure that your multi-cloud is optimised to provide the most streamlined and efficient service possible to your business.

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Post-COVID Mortgage Processing: Ripe for Intelligent Automation to Boost Organisational Resiliency

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Post-COVID Mortgage Processing: Ripe for Intelligent Automation to Boost Organisational Resiliency 3

By Asheesh Mehra, Group CEO and Co-founder, AntWorks

As seen in many other countries, the COVID-19 pandemic sent a shockwave through the UK housing market, bringing the entire sector to a virtual standstill. As lockdown restrictions  ease, we are now witnessing a housing boom like no other, as thousands have entered the market looking to capitalise on the UK government’s new stamp duty relief on properties priced up to £500,000. At the same time, however, the economic fallout from this financial crisis has resulted in almost 750,000 people losing their jobs and countless more being furloughed, leading to an increase in property remortgaging requests and payment holidays.

As a result, banks and mortgage companies now find themselves inundated with new mortgage applications, refinancing and forbearance applications. To support this, there is now a drastic need for increased manpower to manually process the plethora of mortgage enquiries in a more efficient manner. That being said, the uncertainty of future pandemic impact and restrictions being imposed at a local or global level is leaving the industry under severe pressure to deal with the demand as quickly and effectively as possible.

Like many other industries feeling the impact of the COVID-19 crisis, the mortgage sector needs to deploy digitisation in their operations in order to scale their business faster than before or risk being left behind. Artificial Intelligence, deployed in conjunction with intelligent automation, can help ease the burden on mortgage brokers and lenders by accelerating the mortgage loan process and reducing costly errors caused by manual input.

Achieving speed and scale through intelligent automation

Automation is a viable and logical  solution for mortgage lenders as approximately 60 – 70 per cent of tasks in mortgage processes across the value chain are, replicable and prep-based tasks that are suitable candidates for automation. Traditionally, mortgage companies frequently conduct borrowing assessments that require careful analysis and comparison of customer data. This includes checking and establishing customer credit history as well as affordability by manually processing data from income documentation such as bank statements and payslips. This is a tedious but highly necessary process known (rather un-fondly) in the industry as the “stare and compare” stage of mortgage processing

These tasks require a huge amount of paperwork and form filling, which is not only time-consuming but also prone to human error. Furthermore, in their day-to-day routine, mortgage processors are required to literally unpackage and organise documents that are often in paper formats. This is a laborious process especially when executed across multiple mortgage applications at the same time.

This is where intelligent automation steps in to help mortgage companies take on and complete far more work, at a much faster rate and with higher accuracy. Automation can relieve mortgage workers from repetitive tasks such as manually populating the loan origination systems. This means that customers can get loans approved quickly and efficiently. In fact, a global mortgage provider leveraged the power of automation to increase the speed at which mortgage documents were being generated by up to 90 per cent without compromising the integrity of its review process. What’s more, they also managed to improve the turnaround time for the more complex documents by 100 per cent.

Cognitive Machine Reading (CMR) based solutions are the answer for companies looking to achieve straight-through processing for all their mortgage documents. CMR enables mortgage companies to overcome the challenges of digitising unstructured data and achieve faster ROI with higher accuracy with data certainty. Additionally, it can help mortgage companies to cut down on loan processing costs by up to two-thirds and mortgage origination time by 50 per cent.

The (fractal) science behind CMR is that it uses integrated AI capabilities to process highly complex unstructured data along with the more basic data formats. This data can then easily flow through the entire organisation via an end-to-end process achieved with little to no human interference.

Inevitably, all business data needs to be digitised so that it can feed analytics, drive automation, and provide those much-needed customer insights. CMR can play a part to eliminate repetitive and error-prone stare-and-compare tasks that are often a commonplace in mortgage processing. It is able to identify and process the context of data and validate it against existing information elsewhere. As a result, this speeds up the overall processing time for new mortgage and refinancing requests.

Avoiding common automation mistakes

Before kickstarting any digital transformation journey or automation projects, it is imperative that businesses look into avoiding the pitfalls of adopting the wrong automation tools. For example, utilising traditional Optical Character Recognition (OCR) technology for business processes can lead to significant data challenges which will slow down and impede automation goals. OCR is a simple data ingestion tool that is limited to only processing and automating structured data that comes in the form of fixed-field text. Given that 80 per cent of the data within most organisations is unstructured or does not have a predefined format (e.g. emails, images, signatures, social media feeds), OCR technology cannot ingest the vast majority of data trapped inside a mortgage process (or any other business process). In order to overcome this and improve its business process outcomes, one leading Insurance provider managed to process large volumes of unstructured data via CMR automation to achieve 75 per cent reduction in the manual data extraction of handwritten documents. Additionally, the company also achieved more than 92 per cent accuracy in identifying and processing handwritten content.

Critical, everyday business data contained in multiple formats such as emails, images, and handwritten material make up a large part of unstructured data. This is why businesses need to put greater emphasis on researching and identifying intelligent automation solutions that can unlock this date to achieve their business goals. CMR enables mortgage companies to significantly accelerate the course of identifying and classifying all types of documents by cutting down the reduction time for processing mortgage claims by 90 per cent with a substantial level of accuracy (75%). What’s more, it enables any organisation to automate at scale, bringing true automation as a company-wide approach rather than a segregated one.

The COVID-19 pandemic has managed to speed up the need for businesses to embrace digital transformation. This may well be the catalyst for many mortgage organisations steeped in antiquated legacy-based ways of working to refine and streamline their business operations via straight-through processing. It is clear that companies can successfully automate entire business operations to not only improve their operational efficiency but also achieve organisational resilience in a long run. And the faster mortgage lenders can tackle their processes right now, the better for the sooner they can pass those efficiencies and savings onto customers to help rebuild the economy and bolster the housing market in the UK and elsewhere.

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