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WHEN IS THE RIGHT TIME TO ENLIST THE SUPPORT OF IT WHEN IT COMES TO DATA?

Jelle De Jong, CEO, Lexia Analytics looks at the key steps for implementing effective data reporting

Introduction

When it comes to maximising the true value of an organisation’s data, many CFOs and business leaders are quick to turn to the IT team to deliver solutions that will turn disparate siloes of Excel spreadsheets into meaningful insights. However, at best, this will result in your data project falling into the dreaded IT implementation queue, at worst, the solution that’s delivered is not fit for purpose.

The journey must begin with detailed, contextual insights and active reporting, ending with system solutions from IT that are based on factual and accurate representations of need.

Jelle De Jong

So, what are the steps a business needs to take before approaching IT that will make the journey to data equilibrium much quicker and more straight forward?

Five steps to take before approaching IT

It is counter-productive and counter-intuitive to suggest first setting up the correct system and processes before proper reporting can be introduced, which will often result in delaying the quick progress that can in fact be made early on.  

  1. Establish the most important questions that will need answering by your data implementation. This is a much more agile approach which will provide the material difference to effective decision making
  2. Build a first draft analysis, often in Excel or a simple SQL database. Only once this report or analysis has proven its work and is being used as expected can a system solution be considered. This also means any system requirements and design to structurally deliver such a report or analysis is significantly informed by the work done to date
  3. Work with the actual users of the system and see how this helps them. Success breeds success and often to show 1-2 examples of how the data / insight can be converted into actual bottom-line benefit goes a long way
  4. Keep working in paper and basic xls templates for at least six months before you start to think about a real hard-coded solution. This will enable you to find and address issues before the IT deployment begins
  5. Start small, scale afterwards. It is much better to build the basic functionality first and only once this is up-and-running to move towards a hard-coded solution

With the route to IT now mapped out, it’s important to drill down into each of those steps, starting with identifying the top three specific questions that will help inform the system requirements needed from any IT implementation. But how do you successfully achieve this?

How to identify specific questions to inform IT

IT developments are often like pouring concrete. In the pouring phase, every shape is achievable but once it has set, it is fixed.  As such, it is important to identify what the mini-steps are rather than trying to define an overall end-result in advance. 

With analytics and overall data solutions, rather than build a major data repository, only to establish how you are going to use it after it has been built, it is much better to start small. For example, build a small ‘data lake’ and work that first.

The key question you should ask therefore is: what are the most obvious, easy-to-action use cases that we can identify with minimal IT involvement in order to prove the relevance to the business?

Analysing and using the first draft analysis

Now that you have your first cleansed and structured database with crucial data to address one of the pre-identified use cases, it is up to the analysis team and the business stakeholders to show that this approach to solving business issues has merit. For example, the first overview of all pricing data and profitability must show the business how it can increase profitability by developing a better pricing structure. Similarly, the first aggregation of all production data for a packaging line in the factory must lead to improved productivity and reduced downtime.

Only once these initial wins become visible and tangible can you move to scaling up and discussing internally how to build a more sustainable solution.

Once you’re clear on the journey you are taking, it’s important to know when the time is right to mobilise the IT department.

How to know when to get IT involved

It always makes sense to engage IT relatively early on as they have a unique perspective on what is easily achievable and what is not. However, moving from ad hoc, fit-for-purpose solutions to a real system implementation should only happen when it is absolutely clear what a successful first implementation would be and what the longer-term roadmap might look like.

Agile, small improvements and builds can then start where, ideally, every step itself is a viable solution that already has value for the business.  2-3 months is the average timescale before fully involving IT.

Conclusion

The age-old phrase ‘fail to prepare, prepare to fail’ is critical in data solution deployments. It’s critical for leaders to start at the beginning, with detailed context that will help inform accurate representations of need. Following the five steps will help ensure your brief to IT will deliver a successful solution that meets the needs of the business in terms of data, analysis and insight-driven decision making.  

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Finance

AI: CUSTOMER FACING EMPLOYEES’ BEST FRIEND IN THE FINANCIAL SERVICES INDUSTRY

By Ryan Lester, Senior Director, Customer Experience Technologies at LogMeIn

 

We’ve all heard the old saying “money talks.” Well when it comes to customer loyalty and retention, good customer experience talks much louder, with 30% of customers leaving a brand and never returning due to a bad experience.

The truth is, there are a lot of companies with similar products and services, but that doesn’t mean that differentiation is impossible. So, what’s the solution? For financial services, large and small, customer experience is becoming the key competitive differentiator and the best way to deliver an impactful experience is to empower customer-facing employees to do their best work. Artificial intelligence (AI) is enabling these employees to create remarkably better customer experiences, resulting in customer loyalty, advocacy, and overall growth.

For financial institutions that have been considering new strategies for improving the quality and efficiency of their customer experience, here are a few ways AI can enable them to deliver the “human factor” that good customer experience demands whilst ensuring customer facing employees can provide a more positive experience for customers.

 

Increase employee productivity

How much of employees’ time is spent searching for answers to questions? Do they ever have to put customers on hold or even step away to get additional help? AI helps provide front-line employees real-time guidance so they can spend less time looking for information and more time solving problems. An AI-powered chatbot, for example, can be listening in the background of a conversation helping point employees to the right data, solutions, and processes to resolve customer issues faster than ever before.

 

Deliver a consistent customer experience

When banking customers engage with their financial institutions, they measure the speed and accuracy of the service through two criteria. First, how quickly can the system access their account and deliver the correct information? Is it faster than a human could type it in and share it? And second, if they eventually do need to be connected to a live customer support agent, is their information captured and passed along accurately? AI technology takes those general queries off the customer support team’s plate, providing a quick, accurate, and effective response. If a query needs a more in-depth response, AI can hand it off to support staff to address.

Not only this but leveraging a centralised, AI-powered knowledge solution ensures every employee has access to the same, updated information, so no matter who the customer speaks to, they can be assured that employee responses are both consistent and accurate across the board.

 

Accelerating employee training and onboarding

Like any industry, employee turnover is inevitable and can be costly. But, not training new employees correctly or in a timely manner could be much more costly. When it comes to financial services there is a lot to learn, whether it is something simple like the process for checking an account balance to all the nuances associated with mortgage loans. AI can support on-the-job training by helping new employees answer questions confidently, correctly, and much quicker than they could before.

 

Improving employee satisfaction

Today’s banking customer has all kinds of new ideas about their banking experience. “The Amazon Effect” has successfully raised consumer expectations to the extent that a consistent, personal, and relevant experience is the new normal. As a customer, how many times have you been told “I’m sorry, I don’t know the answer?” Customers want solutions to their problems and employees want to be able to deliver those solutions as efficiently and effectively as possible. AI assisting in the background helps minimise those negative moments – making employees job easier, less stressful, and overall more enjoyable.

 

Identify knowledge gaps

Do you know all the questions employees are getting asked? Do you know what’s easily answered and what’s not? Real-time insights allow knowledge managers to keep up to date on frequently asked questions and gaps in current resources. This allows them to strategically improve or add content where needed.

 

Augmenting customer service

Whether talking with an AI chatbot or a personable customer service team member, the modern banking customer has high expectations for convenience, speed, and security. Which means that the technology you choose to deploy and how you deploy it is now just as important as who you hire and how you train them.

Today’s AI solutions won’t replace customer service agents or get in the way of the human factors that drive the customer experience. On the contrary, they augment it, allowing the business to do more without adding human resources. The higher the quality of a AI chatbot solution, the better it will be at taking the routine requests off the plate of customer service agents—giving them more time to provide a personalized and positive experience for customers.

 

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Technology

BEFORE THE INK IS DRY: CORRECTING BIOMETRIC SPOOFING MYTHS

Eric Setterberg, System Design Engineer at Fingerprints

Biometric authentication is highly robust, and the latest solutions offer considerably greater security than their authentication predecessors: PINs and passwords.

But as biometrics moves into new areas such as payments and access control, privacy and security concerns are rising. Biometrics has long been subject to scrutiny, with many elaborate examples of people working to trick biometric sensors to crack devices in the media and online.

To ensure the continued adoption of biometrics, it is important to shine a light on the reality of biometric spoofing.

 

The Evolution of Biometric Solutions…

The first use of fingerprints as forensic evidence was in an Argentinean court case in the late 1800s. With the technology still in its infancy, this was done manually and by eye, comparing latent residual prints lifted from crime scenes to charts of inked fingerprints obtained from the suspects at arrest.

A few decades later, the FBI began collecting fingerprints of criminals and civilians. They also introduced the automated comparison of fingerprints by computers in the 1970s. These “traditional representations” have now been standardized by ISO and ANSI.

… and their Spoofs

The earliest and simplest of these matching devices were easy to spoof. Really, all you needed was a photocopy or a good image of a fingerprint to make a successful spoof.

But as biometrics moved to more advanced technology, the game for biometric ‘spoofers’ has changed and the task of crafting fake fingerprints is considerably more difficult.

The biggest boost for biometric security, however, came with its introduction into mobile phones.

 

How Mobile Changed the Game

Before the widespread integration of fingerprint sensors in smartphones, the technology underwent significant evolution. No operator wanted to use large biometric sensors in modern phone designs. Sensors had to become much smaller to reach the perfect price and design point for the mobile world, but this meant needing to capture data from a smaller surface area of the finger.

To maintain the security of these smaller sensors, algorithms evolved significantly in order to utilize a greater amount of data per unit area. These mobile-driven hardware and software changes resulted in the optimized image capture of modern touch sensors.

As a result, tricking these systems now requires a considerably higher level of detail to be reproduced correctly for a match to be successful, far beyond rudimentary gummi bear spoofs and photocopies

 

Setting the Perfect Spoofing Scenario

Compromising fingerprint authentication via spoofing can still be done, even with all the technological advancements. However, it now requires considerable care, skill, money, and time. And to start, a good latent print…

To retrieve a latent print that’s high quality enough to work, you either need a willing volunteer to lend you their finger, or the commitment to stalk a victim until a viable fingerprint can be retrieved. Even with a decent latent print, modern spoofs then require advanced photoshop skills and/or a lab to successfully convert latent prints into effective moulds.

So – what about those articles boasting how easily they have hacked the latest smartphone device’s fingerprint sensor?

In fact, there are only two instances of fingerprint spoofing seen in the media nowadays: proof of concept and cooperative spoofs. Lay enthusiasts and media go through the effort of setting up a lab to create spoofs with latent fingerprints either from themselves or cooperative volunteers. Even the most successful of these take months of work, a highly skilled team, and the perfect scenario of circumstances.

Put simply, the effort required for spoofing modern fingerprint sensors cannot be applied at any scale. Each biometric spoof needs to go through the same laborious process and clinical conditions. So, if you can bring together a willing group of spoofing enthusiasts, tricking a biometric device could earn you fifteen minutes of fame on the internet, but it is likely to be conducive to a successful criminal business plan…

 

A “How” Without a “Why”

Spoofing biometrics remains technically possible, and there will always be those up to the challenge of trying to hack the latest technology. But the reality is that modern biometric solutions require more time, skill, and frankly, luck, to successfully spoof than ever before. Not to mention that tireless R&D work is continuously strengthening spoofing resistance. And, as use cases start to combine multiple biometric authenticators, such as combining fingerprints with face or iris to perform an authentication, spoofing will only become more complex.

By comparison, hacking PINs and passwords is considerably simpler and more scalable, making it far more lucrative. And, criminals generally take the path of least resistance.

For the average consumer, greater use of biometric authentication is not only a means of simplifying authentication, but dramatically improving the security of their devices, applications, and personal data. With PINs and passwords still the most common authentication method outside of mobile, it is imperative that the true security and advanced nature of modern biometric authentication solutions are understood.

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