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PUTTING YOUR MONEY ON BIG DATA AND ANALYTICS FOR GUARANTEED WINS

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PUTTING YOUR MONEY ON BIG DATA AND ANALYTICS FOR GUARANTEED WINS

By Rahul Singh, President, Financial Services, HCL Technologies

Banks and financial services have always had a voracious appetite for data. They have a finger on the pulse of company balance sheets, interest rates, money supply, exchange rates, inflation rates, stock market movements, bond yields, customer transactions…you name it, they are tracking it. But most of their data management and analytical systems have historically been geared for daily, weekly, monthly and yearly trend analysis and forecasting. What they need today is real time customer visibility and response based on a continuous and growing stream of rich data. But, real time data at current volumes, variety and velocity is so overwhelming that even financial institutions weaned on data can be intimidated by it.

Rahul Singh

Rahul Singh

Today’s anytime, anywhere availability of financial services—over the web, on mobile devices, at kiosks and ATMs—means that a single misstep or loss of time in response can translate into missed business. Take the case of a US-based client who found that although traffic to their site was growing, the sale of online auto insurance products was on the decline. Besides, thanks to a poorly-performing web property, customers were falling back on contact centre executives to assist with questions and purchase, resulting in an increase in the cost per customer. It was clear that their existing data and analytical tools were inadequate to identify the problem and arrest the decline in online sales.

When we looked at the problem we wondered, “What is the reason for the success of the call centre that digital lacks? What can digital learn from the contact centre?” We installed a Big Data and insights solution for the client, following customer journeys across touch points, acquiring quick-stream data to uncover the problem. We analysed customer behaviour at every point, what they did, what they needed, where they fell off, what intervention the call centre may have produced at this point, how to serve the same solution online in real-time, and so on.

Ultimately we solved the problem by analysing the data. Online sales increased by 50% (and spiralling call centre costs were controlled).

At the heart of the solution was a Unified Information Architecture enabled by Next Gen capabilities such as Big Data Lakes that ensure a vast amount of data can be acquired and quickly analysed.

Big Data Lakes differ from a traditional Enterprise Data Warehouse (EDW) in that they store raw data with no definition or schema. Each user creates the schema for data that they need, when they need it. In other words, while a traditional EDW will extract, transform and load the data, the Big Data Lake will first load the data and then transform it to deliver accurate, personalised, real-time customer insights.

For our client, this has become a foundational project, a starting point for their Big Data journey that will inevitably make it simpler to deploy cognitive technologies for higher levels of insights at a future point in time. Not only are Big Data platforms cost effective in comparison to the relational systems, they can scale to multi petabytes which is a requirement when you start introducing web clicks and other unstructured data as an added benefits.

Big Data and Analytics are being used to unlock better views of the customer in the US. The priorities in Europe are a little different. Here, we are using our experience in Big Data and Analytics to bring banks up to speed on what is possible, using our US experience. However, in Europe the hot target use case is regulatory. Compliance is a priority in Europe, followed by crime analytics and then marketing.

In the US, financial institutions are asking questions such as, “How can I make my P&C customer buy my wealth management services?” In Europe, they are asking, “How can injecting petabytes of data improve risk management and regulatory compliance?” The answer to both lies is Big Data Lakes and Analytics.

Simply put, organisations need to focus on:

  • Agility to ingest, process and analyse any data, any time, at any level of complexity
  • Flexibility to correlate, combine and consolidate all data for rapid delivery
  • Ability to easily and quickly publish data for end-user reporting and analytics applications
  • Expertise to gain the insights needed to grow market share, improve operations, and increase customer satisfaction

Regardless of use case (customer visibility, improved sales/ personalised product creation, reduced cost of customer service, regulatory compliance, crime analytics), getting ahead in today’s dynamic business environment means being able to innovate and operate at the speed of digital commerce. In turn that means becoming a data and analytics-driven enterprise delivering on-demand Agile Analytics services to business users and customers.

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How to Build an AI Strategy that Works

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How to Build an AI Strategy that Works 1

By Michael Chalmers, MD EMEA at Contino

Six steps to boosting digital transformation through AI

In the age of artificial intelligence, the way we interact with brands and go about our work and daily lives has changed. No longer blithe buzzwords, AI tools and algorithms are solving real business problems, streamlining operations, boosting productivity, improving customer experience, and creating opportunities for advantage in a competitive marketplace.

However, many businesses struggle to unlock the full benefits that come with its adoption across the whole organisation. Making the most of AI requires a strategic focus, alignment with the specific operating model of the business, and a plan to implement it in a way that delivers real value.

Not all AI strategies are equal. To be successful, businesses need to set out how the technology will achieve objectives and identify the specific assets and case uses that will set them apart from competitors. The process of creating and delivering a successful AI strategy includes the following six essential elements that will help to bake in business success.

  1. Start with your vision and objective

One slip-up companies often make when developing an AI strategy is a failure to match the vision to the execution. Almost inevitably, this results in disjointed and complicated AI programmes that can take years to consolidate. Choosing an AI solution based on defined business objectives established at the start of a project reduces the risk of delay and failure.

As with any project or initiative, it’s crucial to align your corporate strategy with measurable goals and objectives to guide your AI deployment. Once a strategy is set and proven, its much quicker and easier to roll it out across divisions and product teams, maximising its benefits.

  1. Build a multi-disciplinary team 

AI is not an island. Multi-disciplinary teams are best placed to assess how the AI strategy can optimally serve their individual needs. Insights and inputs from web design, R&D and engineering will together ensure your plan hits objectives for key internal stakeholders.

It’s also important to recognise that with the best will and effort, the strategy might not be the perfect one first time around. Being prepared to iterate and flex the approach is a significant success factor. By fostering a culture of experimentation, your team will locate the right AI assets to form your unique competitive edge.

  1. Be selective about the problems you fix first

Selecting ‘lighthouse’ projects based on their overall goals and importance, size, likely duration, and data quality allow you to demonstrate the tangible benefits in a relatively short space of time. Not all problems can be fixed by AI, of course. But by identifying and addressing issues quickly and effectively, you can create beacons of AI capability that inspire others across the organisation.

Lighthouse projects should aim to be delivered in under eight weeks, instead of eight months. They will provide an immediate and tangible benefit for the business and your customers to be replicated elsewhere. These small wins sow the seeds of transformation that swell from the ground up, empowering small teams to grow in competency, autonomy and relatedness.

  1. Put the customer first, and measure accordingly

Customer-centricity is one of the most popular topics among today’s business leaders. Traditionally, businesses were much more product-centric than customer-centric. Somebody built products and then customers were found. Now, the customer is, and should be, at the heart of everything businesses do.

By taking a customer-centric approach, you will find that business drivers determine many technology decisions.  When creating your AI strategy, create customer centric KPIs that align with the overall corporate objectives and continually measure product execution backwards through the value chain.

  1. Share skills and expertise at scale through an ‘AI community of practice’

The journey to business-wide AI adoption is iterative and continuous. Upon successful completion of a product, the team should evolve into what’s known as an ‘AI community of practice’, which will foster AI innovation and upskill future AI teams.

In the world of rapid AI product iterations, best practices and automation are more relevant than ever. Data science is about repeatable experimentation and measured results. Suppose your AI processes can’t be repeated, and production is being done manually. In that case, data science has been reduced to a data hobby.

  1. Don’t fear failure: deploying AI is a continuous journey 

The formula for successful enterprise-wide AI adoption is nurture the idea, plan, prove, improve and then scale. Mistakes will be made, and lessons learned. This is a completely normal – and valuable – part of the process.

Lighthouse projects need to be proven to work, processes need to be streamlined and teams need to upskill. Businesses need a culture of learning and continuous improvement with people at the centre, through shorter cycles, to drive real transformation.

An experimental culture and continuous improvement, through shorter cycles, can drive real transformation. A successful AI strategy acts as a continually evolving roadmap across the different business functions (people, processes and technology) to ensure your chosen solutions are working towards your business objectives. In short, let your business goals guide your AI transformation, not the other way around.

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Iron Mountain releases 7-steps to ensure digitisation delivers long-term benefits

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Iron Mountain releases 7-steps to ensure digitisation delivers long-term benefits 2

Iron Mountain has released practical guidance to help businesses future-proof their digital journeys. The guidance is part of new research that found that 57% of European enterprise plan to revert new digital processes back to manual solutions post-pandemic.

The research revealed that 93% of respondents have accelerated digitisation during COVID-19 and 86% believe this gives them a competitive edge. However, the majority (57%) fear these changes will be short-lived and their companies will revert to original means of access post-pandemic.

“With 80% still reliant on physical data to do their job, now is a critical time to implement more robust, digital methods of accessing physical storage,” said Stuart Bernard, VP of Digital Solutions at Iron Mountain. “Doing so can enhance efficiency and deliver ROI by unlocking new value in stored data through the use of technology to mine, review and extract insight.”

Why revert?

When COVID-19 hit, companies had to think fast and adapt. Digital solutions were often taken as off-the-shelf, quick fixes – rarely the most economical or effective. But they are delivering benefits – those surveyed reported productivity gains (27%), saving time (20%), enhancing data quality (13%) and cutting costs (12%).

So what now?

The Iron Mountain study includes guidance for how to turn quick-fixes into sustained, long-term solutions. The seven-steps are designed to help businesses future-proof their digital journeys and maximize value from physical storage:

1)     Gather insights: The COVID-19 pandemic allowed organisations to test and learn. Companies should ensure these insights are fed into developing more robust solutions.

2)     Use governance as intelligence: Information governance and compliance are fundamental to data handling. But frameworks aren’t just a set of rules, they hold valuable insights that can be turned into actionable intelligence. Explore your framework to extract learnings.

3)     Understand your risk profile: A key early step is to analyse where you are most vulnerable. With data in motion and people working remotely, which records are at risk? What could be moved into the cloud? Are your vendors resilient?

4)     Focus where you will achieve greatest impact: To prioritise successfully, you need to know where you will achieve the largest impact. This involves looking beyond initial set-up costs towards the holistic benefits of digitisation, including reducing time spent on manual scanning, and the risk of compliance violations.

5)     Reach out and collaborate: We are all in this together. Your IT, security, compliance and facility management teams are all facing the same challenges. Ensure you collaborate across functions to develop robust, integrated solutions.

6)     Find a provider who can relate to your digital journey: For companies that still rely heavily on analogue solutions, digitisation can be daunting and risky. It pays to find a vendor who has been on the same journey, understands your paper processes and can guide you through the digital world.

7)     Prioritise and evolve communication and training programmes: To reap the full rewards from any digitisation initiative, thorough and continuous communication and training is critical. Encouragingly, our survey found that 81% of data handlers have received training to work digitally which is an excellent step in the right direction, but consider teams beyond data handling to truly succeed.

The research was commissioned by Iron Mountain in collaboration with Censuswide. It surveyed 1,000 data handlers among the EMEA region. It found that the departments that have digitised more due to COVID-19 include IT support (40%), customer relationship management (36%), and team resource planning (34%).

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3D Secure: Why are fraudsters still slipping through the net?

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3D Secure: Why are fraudsters still slipping through the net? 3

By Tim Ayling, VP EMEA, buguroo

There is a constant tension between keeping online payments secure, and offering an easy and frictionless user experience. Digital transformation – especially accelerated by the global pandemic – leaves consumers expecting online services to be seamless. Customers are even liable to abandon a process altogether if they encounter a hurdle.

Financial regulation and security protocols exist to help ensure that a balance is maintained between offering customers this frictionless experience, and keeping them and their funds safe from fraud attacks.

What is 3D Secure?

3D Secure is one such protocol. This payer authentication system is designed to keep card-not-present (CNP) ecommerce payments secure against online fraud. The card issuer uses 3D Secure when a card is used to pay for something online, authenticating the customer’s identity based on personal identifiers, such as the three-digit CVV code on the back of a card, as well as the device they’re using to make the payment and their geolocation or IP address.

3D Secure is important because although transactions can be accepted or denied based on the level of risk, it’s not always as clear as ‘risky’ or ‘not risky’. A small number of transactions will have an undetermined or questionable level of risk attached to them. For example, if a legitimate customer appears to be using a new device to buy goods online, or appears to be attempting to make the transaction from an irregular location. In these instances, 3D Secure provides a step-up authentication, such as asking for a one-time password (OTP).

Getting the right balance

3D Secure is a helpful protocol for card issuers, as it allows banks to comply with Strong Customer Authentication as required by EU financial regulation PSD2 as well as increase security for transactions with a higher level of risk – thereby better filtering the genuine cardholders from fraudsters.

Tim Ayling

Tim Ayling

This means that the customers themselves are better protected against fraud, and the extra security helps preserve their trust in the bank to be able to keep their money safe. At the same time, the number of legitimate customers who have their transactions denied is minimised, improving the customer’s online experience.

So why are fraudsters still slipping through the net?

Fraudsters are used to adapting to security protocols designed to stop them, and 3D Secure is no exception. The step-up authentication that is required by 3D Secure in the instance of a questionable transaction often takes the form of an OTP, a password or secret answer known only by the bank and the customer. However, there are various ways that fraudsters have devised to steal this information.

The most common way to steal passwords is through phishing attacks, where fraudsters pretend to be legitimate brands, such as banks themselves, in order to dupe customers into giving away sensitive information. Fraudsters can even replace the pop-up windows that appear to legitimate customers in the case of stepped-up authentication with their own browser windows disguised as the bank’s. Unwitting customers then enter the password or OTP and effectively hand it straight over to the fraudsters.

Even when an OTP is sent directly to a customer’s phone, fraudsters have found a way to intercept this information. They do this through something called a ‘SIM swap scam’, where they impersonate their victim and manage to get the legitimate cardholder’s number switched onto a different SIM card that they own, thereby receiving the genuine OTP in the cardholder’s place.

This is especially an issue for card issuers when taking into account the liability shift that is attached to using 3D Secure. When a transaction is authenticated using 3D Secure, the liability moves to lie with the card issuer, not the vendor or retailer. If money leaves a customer’s account and the transaction was verified by 3D Secure, but the customer says they did not authorise the transaction, the card provider becomes liable for any refunds.

How AI and Behavioral Biometrics can be used to plug the gap

Banks need to find a way to accurately block fraudsters while allowing genuine customers to complete online payments. AI can be used alongside behavioural biometrics as an additional layer of security to cover the gaps in security through continuous authentication of the customer.

Behavioural biometrics can collect and analyse data from thousands of parameters around user behaviour such as their typing speed and dynamics, or the trajectory on which they move the mouse, throughout the entire online session. AI processes are used to dynamically compare this analysis against the user’s usual online profile to identify even the smallest of anomalies, as well as against profiles of known fraudsters and typical fraudster behaviour. AI then delivers a risk score based on this information to banks in real time, enabling them to root out and block the fraudulent transactions.

As this authentication occurs invisibly, the AI technology can recognise if the customer is who they say they are – and that it isn’t a fraudster trying to input a genuine OTP they have managed to steal through phishing or SIM swapping – without adding any additional friction.

Card issuers cannot decline all questionable transactions without losing customers, while approving them without additional checks poses security issues that can result in financial losses as well as losses in customer trust. Behavioural biometrics is a foundational technology that can work simultaneously to 3D Secure to keep customers’ online payments safe from fraud while maintaining a frictionless experience and minimising the risk of chargeback liability for banks.

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