People are paying more for their homes around the world, with average house prices up 6.5% in the last 12 months.

But, where have house prices grown faster than the average income?

Assured Removalists have combined data on average annual salary, income tax and house prices to produce a ratio that shows the measure of housing affordability around the world. The higher the ratio is, the less affordable the houses are.

How does your country compare? You can view the full data set here.

Card fraud has increased 19% year on year, according to The Nilson Report, accounting for losses of around $16.3 billion, in 2015. France has seen an 8.9% increase in card fraud and the US, which has the largest fraud/loss ratio, currently accounts for 47.3% of the world’s payment card fraud losses.

The threat to banking is at least in part due to the explosion of data, according to Sopra Banking. It is expected that by 2020 we will be creating more than 44 times the data we created in 2009 - and that fraud will have resulted in losses of $35,4 billion. The storage and transmission of so much offers opportunities for fraud and cybercrime as well as being part of the problem.

The Evolution of Fraud Management

Ensuring that customer protection is paramount, whilst also preventing normal transactions from being interrupted is a fine balancing act for banks. The evolution in handling fraud management can be conducted in a more intelligent manner using big data - or ‘dataprints’.

Alike fingerprints, dataprints give us unique information about a given person, action, place and point in time. Analysing these accurate identifications (transactions, devices, usual patterns) through Artificial Intelligence, provides a warning sign of fraud for banks and customers.

Analyst firm McKinsey in their look at disruptive technologies, predict that neural networks will utilize big data to enable “knowledge work automation”. Learning and applying new and more refined algorithms improves the process’s sophistication and capabilities, making it easier to make data-driven decisions to detect fraud.

It’s all very well to say that data and technology can help prevent fraud - but what does this look like in practice, and how can banks achieve this?

1. Collection and Centralization of Internal Data

It is necessary to devise ways of collecting and storing big data in a manner that allows you to take full advantage of it when you need it - but also keep it secure.

Normally, data is created and held in silos, in a division/department/business area/type manner and because of this delocalization, it ends up being difficult to collate, distribute and utilize in any sort of global way. Centralizing the collection and management of data means that you can more easily access the data and cross-reference it.

A July 2014 survey of bank respondents by The Economist, found that half had applied centralized analytics to big data management through artificial intelligence software. In turn, these banks had the most holistic approach to risk mitigation and fraud prevention and enhanced their security as a result. It is something the industry needs in order to fight fraud in 2017 and onwards.

The centralization of data and in turn creation of intelligent big data will enable banks to not only mitigate fraud, but service their audience better. The implementation of big data centralization is as much a process as a system and requires synthesis of legal and regulatory compliance, a security and privacy focus, strong management and the best technology.

2. Leveraging External Data

Big data means information from multiple and often highly disparate sources. One of the new challenges for data collection have arrived in the form of social media platforms like Facebook and LinkedIn. However, external data tracking, can be an extremely useful tool in the fight against fraud.

Analyst firm, McKinsey has shown that the use of  external data, such as social media activities, can have up to 35% improvement in areas such as risk mitigation, as well as allowing the development of better insights into customer behaviour and ultimately in fraud behaviour analysis. One of the reasons for lack of uptake in this area is the difficulty of retrieval of such data. Although this is certainly achievable in terms of technology through the use of social graph APIs. However, the consent and release of this data is often a legal minefield and customer privacy worries and media scares themselves can be a hurdle to jump.

Going forward into an era of instant payments, external data tracking that is conducted in a privacy enhanced manner will become even more important. The ability to keep track of these payments, whilst ensuring personal data is obfuscated, all in real-time is a challenging but ultimately empowering new tool for the industry.

3. Using Behavioural Profiles to Prevent Fraud

Big data is revolutionizing the process of ‘Know Your Customer’ or KYC. As KYC becomes KYCd, or Know Your Customer’s data, a more accurate and in-depth approach to consumer understanding can be rewarded by more impactful anti-money laundering (AML) and other types of fraud detection.

Being able to model patterns of behaviour by using predictions based on internal, external and social big data is transforming banking. It not only gives you insight into normal behaviour, but that baseline then allows comparison and identication of patterns, similarities and differences - and fraud. Technologies such as geolocation, can be added to the arsenal, so those incidents when a customer is interrupted from making a legitimate purchase are greatly reduced, whilst real crime is detected.

However, it can also offer challenges in terms of security and privacy. Customers are now more informed about privacy considerations and have become less happy about sharing their personal data with any company, not just a bank. Sopra Banking Software report found that 80% of customers would be willing to share their personal data, as long as they did so using a consented, ‘opt-in’, approach and in doing so they were incentivized by better rates and so on.

New EU privacy and data protection laws, which are an adaptation of the Data Protection EU Directive 95/46/EC, are due to be finalized this year. The new data privacy laws will be more restrictive and will have focus on, for example, data stored in the Cloud. This requires a Privacy by Design (PbD) approach when creating Cloud based systems, especially those that store, transmit and transact data. Handling these more extensive regulations needs a more rethink in the approach to security and privacy.

Conclusion

Although the collection of data, how to centralize and manage it, how to make it safe and how best to analyse and make predictions from it are all challenges, they also offer huge potential. The digital revolution that has brought us big data can also bring us big banking.

(Source: Sopra Banking)

Robo-advice has become one of the more popular and prominent financial technology innovations of the last few years, and it’s easy to see why. However, Lester Petch, CEO at FinchTech, reckons there’s cause for concern, and below talks Finance Monthly through five reasons robo-advice may not turn out to be all it’s promised without confronting some hard-hitting issues.

In theory these platforms offer expanded access to financial advice and fill a widening RDR gap, at a lower cost and with superior ease of use. Citigroup estimates that assets managed by robo-advisors could reach a collective value of $5 trillion over the course of the next decade - and that is certainly something to aim for.

Excitement and optimism should always be tempered with pragmatism however, and practically speaking, there are reasons to be concerned. Many available and in build platforms promise innovation, efficiency, and accuracy, but have some major potential hurdles to overcome.

1. Build cost and overspending on customer acquisition

Robo-advice start-ups are often unknown quantities, and must therefore build from scratch. Many rely on digital and social marketing campaigns, alongside referrals, o generate revenue. The problem is that these campaigns are often expensive - sometimes hideously so. Nutmeg, for example, posted a pre-tax loss of £9 million in the last fiscal year, even as marketing and staff costs hit £10.8 million.

It’s not altogether surprising that when cost of acquisition (CAC) for clients exceeds overall lifetime value (LTV), firms lose money. The assumption is that these expensive omni-channel campaigns will of course be successful, and eventually skew the CAC to LTV ratio back in the company’s favour. This is however a precarious position for any business to find itself in, even one with fantastic technology. Deep pockets are required.

In some cases the aim might perhaps be for the business to accumulate enough assets under management to enable a sale or exit, however this is also a risky strategy. Recent 2016 research by SCM Direct, a UK wealth manager, suggested most UK robo-advisers “will go bust before acquiring the sizeable assets under management to ensure their sustainability”.

2. No real performance history

Sophisticated software is no substitute for experience. Many robo-advice platforms haven’t weathered any serious economic storms. Many have little performance history at all and rely on back testing. How much can you trust in a technology that has never been truly tested in the heat of battle, or weathered an event such as a recession or cataclysmic sell off?

3. Limited suitability

Robo-advice platforms may be at risk of not always accurately assessing risk tolerance – which can cause serious problems in an economic downturn. Recent research from FinaMetrica found that 21.2% of the firm’s 100,000 customers incorrectly estimated their true risk tolerance by a significant margin, when using a psychometric risk test. Platforms could be vulnerable to recommend investments that are beyond or below the client’s capacity for risk, especially in the event that the markets exhibit extreme volatility.

4. Reliance on algorithms

In an age of sophisticated and improving technology, reliance on this tech has led some to treat algorithms with an almost mystical reverence. Many are truly impressive, but can clients truly understand them? No algorithm is perfect, and many are unproven and untested in reality. They’re theoretically created to take human error or preference out of the equation, but human error can be a factor in their design and development. Could a mistake lead to catastrophic consequences for clients and do they know what they are buying into?

5. Lack of differentiation

For all the talk of the market’s innovation and creativity, it’s often hard to tell one robo-advisor from another. The major differences tend to be cosmetic, a technological bell here, a branding whistle there, and little differentiating focus on the client’s needs and priorities.

Those robo-advice platforms that enter the market in the near future with more niche or specialised offerings aimed at specific market segments such as cultural groups or different age brackets, are more likely to gain traction, as well as potentially spend less on client acquisition

In conclusion, robo-advisors will need to overcome these problems and more to achieve long-term viability. This isn’t to say that the technology isn’t exciting, the need isn’t there or that it doesn’t have huge potential. The right platforms could potentially redefine the market, and digital investment management is a step in the right direction. If digital investment management platforms can iron out the kinks and focus on what works for their own business model, and more importantly their customers, there is a bright future ahead of them.

The answer is that they are so much more. In a study released today, Dun & Bradstreet revealed data that uncovers the changing role finance leaders play in stewarding their organisation’s customer experience, a mandate traditionally viewed as one of the chief marketing officer. Because positive business results are often fuelled by great customer experiences, chief financial officers are increasingly using data and analytics to become customer-obsessed to ensure their organisation’s customer strategy is rooted in insights that will drive favourable outcomes.

The Customer-Obsessed Finance Leader, a study commissioned by Dun & Bradstreet and conducted by Forrester Consulting, found:

• 36% of company finance executives surveyed were identified as customer-obsessed Leaders, making customer-focused initiatives a top priority this year and reporting increases in customer acquisition, retention, and satisfaction.
• Customer-obsessed finance Leaders reported year-over-year increases in revenue, profitability, and cash-flow at a rate of 10 to 30%age points greater than their less advanced peers.
• Data and insights-driven companies are 39% more likely to report year-over-year revenue growth of 15% or more.
• 79% of customer-obsessed Leaders indicated that their organisation will likely increase spending on technology applications to support customer engagement.
• Implementation of a customer-obsessed strategy requires mastery of seven data competencies that must be aligned across people, processes, and technologies.

CFOs, with their leadership position, cross-organisational perspective, and ability to understand complex sets of data, are uniquely positioned to implement insights-driven behaviours and processes within their organisations. Investing in the right tools and technology, as well as augmenting internal data with third-party data and analytics are some of the key actions leading finance executives are taking.

Challenges to becoming truly customer-obsessed persist; disconnected strategies within the organisation, disparate data, inconsistent metrics, and a lack of investment in technology are among respondents’ most cited obstacles.

The study further outlines seven critical data competencies to master, qualities and resulting metrics that set customer-obsessed finance leaders and followers apart, and how-to strategies to focus efforts around using data and analytics to become a customer-obsessed organisation.

The survey, fielded within North America, Europe, and Asia Pacific in February 2017, included feedback from 250 finance executives (CFOs or EVPs of finance) from companies in multiple industries generating $150 million or more in revenue.

(Source: Dun & Bradstreet)

Another financial year has passed, and as you look back, will you seek to do things differently next time around? Below, Dean Snappey, the President and Co-Founder of DocsCorp discusses with Finance Monthly 5 simple accounting tools that’ll make your life that much easier to navigate at this time of year.

Over the past 12 months we have seen considerable adjustments to taxation, such as changes to dividend taxation and the recent increased tax for landlords. Aim to prevent the end of year mess and avoid the kind of errors that carry implications to your and your company’s reputation.

There are several accounting tools and software solutions available at your fingertips to ease the process, stay organised and plan ahead. Make the most of these accounting tools and follow these five easy steps to make pre-emptive tax planning simple.

1. Bundle Documents. Best practice is to ensure any invoices, statements and paperwork are scanned and saved as a PDF document as paper filing systems and loose documents are at risk of being lost or damaged. Document bundling software gives you the ability to collate PDF content and bind all relevant documents such as tax returns, invoices and financial accounts into a single file for each client. By bundling all important documentation, it will make the process of finalising paperwork much easier as all documentation required will be stored together. Finalised documents can then be sent as a single secure file in one hit rather than multiple attachments, saving you time with each client and avoiding the risk of lost or separated files.
2. Redaction. As part of the process of preparing documents with such complex data, it is essential to redact classified or sensitive information. To redact is to remove, therefore ‘redacted’ is often used to describe documents from which sensitive information has been expunged. By using a redaction tool it is easy to search whole documents to redact multiple instances of a word or string of words in a way that is quick, easy and fail-proof. Redact personal information or bank account details in documents as part of an audit.
3. Make Scanned Documents Searchable. When you scan invoices, receipts and finalised paperwork onto your desktop, the scanned documents are image documents that do not have the text layer that’s needed to make them searchable. You can make the documents text-searchable with an Optical Character Recognition (OCR) tool. This software converts scanned, printed or handwritten files into its machine readable text format.  You can then search whole documents for a word, phrases or a set of numbers which makes finding information or documents for tax audits a breeze.
4. Remove Metadata. Metadata may appear hidden, however, every time you annotate, edit or alter a document, your decision is stored invisibly within the very document you’re working on. Metadata removal software removes metadata from files – eliminating any risk of unintentional information leakage. To put it simply, the metadata cleaner will ensure you only send the part of the document or spreadsheet you intend to – exactly what would be printed out on a piece of paper. Remove hidden cells and other sensitive metadata before uploading or attaching files intended to be sent to clients.
5. Compare Documents for Change. Working with numerous clients means navigating through each client’s financial accounts and complex paperwork. Throughout the year many changes can occur within clients’ businesses, often meaning multiple changes to important documentation. Analysing large volumes of documentation, in which precision is required, can be very time consuming. There is a simpler way to undertake this task, which is by using a Comparison software package. This enables you to compare and analyse the differences between two documents with incredible accuracy and reliability. The software is able to show you the smallest change in documents, spreadsheets, PDFs without needing to convert them first. Save time and never miss an important change again.

Rob Mellor, General Manager at Wherescape, explains the process from data to decision, and how any business, large or small, needs to make its data amalgamation efficient in order to move forward.

They say moving house is one of the most stressful things you can endure. Having moved recently, I can confirm the old adage rings true! And this was despite paying extra for packers to do all the 'hard work'. But here's the thing: the packers didn't really save us much time because prior to them arriving, we had to spend weeks sorting and prepping our belongings into the right piles to then be boxed and shifted! This is effectively what data architects have to do if they choose not to automate the building of a data warehouse.

Imagine if I could automate the sorting of all my belongings into neatly organised boxes. Then imagine if I could automate the sorting of many families’ belongings without having to visit their houses - even taking into account the unique requirements that each individual customer has. In effect, this is what solutions like WhereScape do: help businesses to intelligently automate the gathering of data, and allow them to dramatically speed up the time it takes to drive value from it. Automating the process of data gathering can drive real business value and provide a flexible, templated approach to automation, personalised for every business requirement.

To give a real world example, Xerox Financial Services (XFS), a $2bn business spanning 14 countries, has the challenge of putting all kinds of data requests into its data warehouse and producing rapid, accurate business intelligence for its local leasing companies across 14 countries. The rapid growth of the company led to business structures growing up in parallel, creating disparate data and variations in business processes. In the past, these data sets had to be looked at in individual silos because of their breadth and complexity. This meant getting an accurate overall picture took so long that often the information was out of date by the time it was delivered.

XFS now consolidates and transforms all of its data sets into a harmonised model using WhereScape. Integrating multiple tables of data from each source has enabled XFS to create a variety of management reports, including an up-to-date snapshot of sales performance on a daily basis, allowing senior management to ensure targets are hit.

Over the last year, XFS has doubled the number of automated processes yet maintained data quality and decision making. Whereas previously the business had to rely on a monthly 'cycle' of data, it now uploads data every day allowing agile, fast and effective decision-making based on relevant, timely snapshot and trend data. By automating this data collection process, XFS can also engage more effectively with its partners. Through monitoring the value of each relationship, they have a better understanding of the number of proposals sent in by customers, average deal size and the number of order agreements. And the most tangible outcome? The level of automated credit decision-making has increased significantly without compromising the credit quality of the portfolio with complex statistical modelling being supported by data collected daily and transformed by WhereScape. This is a huge leap forward for XFS.

The only value of data is its ability to drive the right business decision. Yet we constantly see businesses failing to do this because of avoidable failures in how they manage it. The automation process XFS has deployed with WhereScape demonstrates that it doesn't have to be that way. There is a choice, and choosing the right process will drive a significantly improved commercial outcome. Now, if I can come up with something similar for helping with my packing the next time I get to move house..!

Technology is often remarked as evolutionary ammo, and the statement stands just the same for the growth of businesses. Finance Monthly below hears from Frédéric Dupont-Aldiolan, VP Professional Services at Sidetrade on the latest and upcoming innovations that have hit 2017 hard.

Artificial intelligence, robotics, machine learning and the Internet of Things: 2016 stood out as a year marked by technological development and significant advances in several fields, not least that of connected, driverless cars. Against this backdrop, a clear trend is appearing: the growing influence of robotic technology in daily life.

In 2017, we have seen more promising innovations, here is my review of the top five things we are seeing:

5. IoT, the Internet of Things

Star of the Consumer Electronic Show (CES), which took place in Las Vegas in January, and Viva Technology, which took place in Paris, the Internet of Things was thrust into the spotlight in 2016 and continues to bring increasingly intelligent connectivity to our daily lives. Smart devices, equipped with bar codes, RFID chips, beacons or sensors, are taking the lead and enabling companies to gain greater visibility over their transactions, staff and assets.

In 2016, information and technology research and advisory company Gartner estimated that there were 6.4 billion connected devices globally, an increase of 30% on 2015. By 2020, this figure is likely to have grown to 20.8 billion.

4. The explosion of Big Data

Network multiplication brings with it a proliferation of data generation, whose analysis, use and governance have become a burning issue. According to estimates by IDC, an international provider of market intelligence for information technology, by 2020, every connected person will generate 1.7MB of new data per second.

The concept of ‘perishable data’ has lost validity. In 2017, companies now have the capability to use data before it becomes obsolete. Devices connected via the Internet of Things will rapidly speed up data decoding and processing for actionable insight.

3. The ramp up of artificial intelligence and automatisation

Artificial intelligence has been one of the main talking points in technology over the last year. Encompassing areas such as machine learning, robotic intelligence, neural networks and cognitive computing, it’s now in daily use in numerous forms including facial and voice recognition, endowing velocity, variety and volume.

This year, artificial intelligence has taken on an increasing number of repetitive and automatable tasks, beginning with wider use of ‘chatbots’ with the capacity to give coherent, easily formulated responses. IDC pinpoints robotics driven by artificial intelligence as one of the six innovation accelerators destined to play a major role in the digitalisation of society and the opening up of new income streams. Indeed, Amazon and DHL are already making use of warehouse handling robots.

2. Location technology, the Holy Grail of customer satisfaction

Location technology has taken great strides over the last year or so, to the marked benefit of customer satisfaction in the hotel, health and manufacturing sectors. Customers can now receive geo-targeted offers on their smartphones, for example for promotions or reductions, depending on their physical location.

In 2017, RFID chips enable yet more accurate tracking of customers and enhancement of their buying experiences.

1. Virtual reality makes way for augmented reality

One of the biggest innovations recently has been virtual reality, and with it came much media coverage too. From Facebook to Sony, Google to Microsoft, big brands grasped this new technology to offer an outstanding user experience, through the merging of virtual and real imagery.

In 2017, these virtual devices have acquired an awareness of their environment and give users a real sense of immersion of the digital environment from within their own homes. The potential of augmented reality for business will be harnessed too in the coming months. Some companies, among them BMQ and Boeing, are already employing it to increase their retention and productivity rates, or to provide training to their workforces across worldwide subsidiaries.

Over the next few months, as we gear up for another round of product launches, we should expect to see advancements in these key areas of technological innovation. Within business, this technology should help to improve customer service by streamlining production and processes, saving time and money, as well as providing new and exciting ways to reach and engage with customers, helping to retain existing clients as well as bring in many new ones.

John Orlando is the Executive Vice President and CFO of Centage Corporation - a leading provider of automated budgeting and planning software solutions. With his previous experience concentrated on Financial Planning and Analysis, John has now been with Centage for over 13 years. Here he introduces Finance Monthly to the company and the services that it offers and discusses the relationship between business decisions and technology.

Could you tell us about the Company’s ethics and priorities toward its clients?

 Centage has been providing budgeting and planning software solutions for over 15 years. We understand that the most important aspect of your job is to develop accurate and timely budgets and forecasts that help you drive the growth and profitability for your company. Everything we do at Centage, from a client perspective; product technology, functionality; through to training, services and support, is dedicated to making the client experience unique. That is our number one priority.

Tell us more about the Budgeting and Forecasting services that Centage offers.

Budget Maestro by Centage is an easy-to-use, scalable, cloud-based budgeting and forecasting solution that eliminates the time-consuming and error-prone activities associated with using spreadsheets. It is designed for small to mid-market companies to support a comprehensive Smart Budgets approach to corporate planning. Its built-in financial and business logic allows users to quickly create and update their budgets and forecasts and never worry about formulas, functions, links or any custom programming. It is the only solution in the market that offers synchronized P&L, balance sheet and automatically generated cash flow reporting. Today, Budget Maestro serves more than 9,000 users worldwide.

How has Centage developed into the company that it is today?

 The company was created because the founders saw a need for a budgeting and forecasting solution that was more automated than what existed in the marketplace at the time. We respected the people and the processes that go into creating accurate and timely budgets and forecasts and thought there was a better way. We understood that giving financial professionals a tool that had all the financial and operational logic pre-built was crucial. This went against the traditional formula-based applications that were in existence. Additionally, Centage developed a full set of synchronized financial statements that included a Pro Forma Income Statement, Balance Sheet and Cash Flow that were automatically generated.

The CFO role in general is important to any company because it brings operational and financial discipline to the organization. I am involved with and required to be familiar with every facet of the organization from financial accounting to operations to human resources, etc. I believe these responsibilities, along with my experience in the FP&A arena building many budgets and forecasts over the course of 25+ years, has helped Centage to build the best budgeting and forecasting application that we could.

What is the role that technology plays in transforming data for better business decisions?

 Technology and business decisions are inexorably linked. All the advances in business over the past 50 years have been related to technology. It has given us the ability to take massive amounts of information from accounting systems, CRM systems and operational systems, condense them in one place and give businesses the ability to instantly review the information for trends and make informed decisions in a much shorter timeframe with little need for manual intervention.

In the case of a CRM system such as Salesforce.com, once you start to use the application it is difficult to fathom how you would have run your sales organization any other way. There are too many pieces of information to keep track of and too many data points could be missed.

Centage similarly has used technology to make our product, Budget Maestro robust and agile by eliminating all the mundane work associated with preparing budgets and forecasts. We specialize in building out all of the financial and operational finance logic so that the client, as the user, only needs to concentrate on building a set of good business assumptions. Our reporting solution, Analytics Maestro, gives our clients the ability to take the data in Budget Maestro or their resident accounting system, and manipulate and analyze the data very quickly, so that more informed business decisions can be made.

What do you anticipate for the sector in the near future?

One thing that has become clear over the past 2-3 years is that budgeting and forecasting is moving from the realm of isolated 12-month timeframes and annual budgets and forecasts, to more of a rolling budget / forecast approach that takes into account anywhere from 18- 36 month timeframes. This allows the user to plan for a much longer horizon.
Secondly, customers have been asking for budgeting and forecasting systems to reach out to other sub
ledger systems such as Salesforce, Payroll etc., to gather information, eliminating the need to manually intervene in the data gathering process.

Visit us at www.centage.com , follow us on Twitter, or visit the Centage Blog for the latest insights on budgeting and forecasting strategies.

Email: jorlando@centage.com

Phone: (508) 948-0024

Artificial intelligence is shaping the future of retail. Smart algorithms and data analyses are creating sustainable performance benefits across all levels of the retail supply chain.

With its Omnichannel ePOS Suite, Wirecard AG is the first payment provider to offer a fully integrated solution for self-learning analyses based on payment data in combination with other data sources. The evaluations substantially support e-commerce and high-street retail in implementing the following central growth concepts: increasing customer conversion, reducing customer attrition rates, predicting future consumer behaviour and linking points of sale with e-commerce.

Jörn Leogrande, Executive Vice President Mobile Services at Wirecard: "Using our data evaluations and analyses, merchants can increase their metrics in important performance areas. Our previous experience has shown that sales increases in the double-digit percent range are realistic."

Wirecard's turnkey solution generates insights into customer segmentation and cohort analyses, for instance, to optimise marketing efficiency. This revolves around the concept of a data-supported, real-time view of a retailer's customer behaviour in its entirety and increasing the customer lifetime value - optimal customer retention.

Insights into customer attrition (otherwise known as customer churn) behaviour are another unique selling point of the Omnichannel ePOS Suite. Complex evaluations enable merchants to identify customers who may potentially shop elsewhere. By introducing appropriate marketing measures, the churn rate can be significantly reduced.

Analyses on anomalies, trends and sentiment, peak detection and time series based on country-specific data as well as cohort analyses to assess the efficacy of marketing measures are additional beneficial tools. The Omnichannel ePOS Suite can be used in pre-existing systems without incurring large expenses.

Markus Braun, CEO of Wirecard: "The Omnichannel ePOS Suite is the first step towards large-scale digital transformation in the retail sector. Over the next few years, data analyses using artificial intelligence and machine learning will play an increasingly important role in their business area. Based on our analyses, we are able to reduce risks and increase the chances of success for our partners. This means that all parties involved can gain a significant competitive advantage, which is why the omnichannel ePOS suite marks a decisive step for the future of payments."

(Source: Wirecard)

This week, IBM Security and Ponemon Institute released the annual Cost of a Data Breach report.

This year’s report found that the UK experienced a decrease in the cost of a data breach, from £2.53 million in 2016, to £2.48 million in 2017. The average cost per lost or stolen record in the UK is estimated at £98.

Key points from the study include:

• Incident Response Leads to Significant Cost Savings: For the third year in a row, the study found that having an Incident Response team in place significantly reduced the cost of a data breach, saving $19.30 USD per lost or stolen record.
• Containment Speed Is Critical: The cost of a data breach was nearly $1 million lower on average for organizations that were able to contain a data breach in less than thirty days compared to those that took longer than 30 days.
• Room for Improvement: On average, organizations took more than 6 months to identify a breach, and more than 66 additional days to contain a breach once discovered.
• By Industry, Healthcare Breaches Most Costly: For the 7^th year in a row, healthcare has topped the list as the most expensive industry for data breaches. Healthcare data breaches cost organizations $380 per record, more than 2.5 times the global average across industries ($141 per record.)
• Top Factors Increasing Cost of a Breach: The involvement of third-parties in a data breach was the top contributing factor that led to an increase in the cost of a data breach, increasing the cost $17 per record.
• Top Factors Reducing Cost of a Breach: Incident response, encryption and education were the factors shown to have the most impact on reducing the cost of a data breach. Having an incident response team in place resulted in $19 reduction in cost per lost or stolen record, followed by extensive use of encryption ($16 reduction per record) and employee training ($12.50 reduction per record).

IBM has also created a “Cost of a Data Breach Calculator,” which can use below.

(Source: IBM)

We’re living in a data rich world. IBM estimates that 90% of the data in the world today has been created in the last two years alone. This means it’s crucial that businesses keep control of their sensitive customer data. Tanmaya Varma,  Global Head of Industry Solutions at SugarCRM, illustrates to Finance Monthly the true potential of data use in the financial services sector.

For banks in particular, the safe and efficient storage of data is not just a ‘nice to have’ but a requirement governed by legislation and industry standards. I believe that whether on-premise or in the cloud, banks should strive to capture all their customers’ data together in one place. Why? Because it will empower employees with the right information to give customers the best experience possible.

Bringing together data streams

Perhaps more than any other industry, financial services firm have a huge number of channels to collect customer data from; in-branch, over the phone, via social media platforms. This means they need to have the right data systems in place which can bind together all of their data to build a complete picture of a customer.

The right system needs to bring together front-office data – calls, meetings, leads, opportunities – and back-office data – accounts, transactions, delivery schedules, fulfilment and so on. There is also a need, particularly for capital markets, to have external data integrated, for example LinkedIn data (where did this prospect use to work?) and trading figures.

In terms of where the data is stored, in my experience banks generally choose to keep their customer data in the cloud. No modern business – bank or otherwise – should keep their customer data in siloes, as this immediately breaks a 360-degree view of the customer.

Meeting customer expectations

Today’s customers expect the best experience possible. The instantaneous pace we now live at doesn’t leave much time for patience – so consumers expect an instant response to their demands.  This means customer-facing employees need to have easy access to their customers’ background as soon as the interaction begins, if they are to stand a chance of delivering the best possible experience.

Customers need to know that, regardless of the channel, they’ll receive the same level of service and understanding of their needs and expectations. This all amounts to the overall customer experience, which is crucial when customers are faced with so much choice. The threat of losing customers because of bad service is very real. According to Accenture’s UK research, 34% of customers who switched financial providers in 2014 did so because of a poor customer service.

All customer-facing teams (sales, marketing, customer service and so on) therefore need to have the right tools in place. Technology should empower employees in their interactions with customers; giving them all the information they need, when they need it. For example, providing clear information on the customers’ previous interactions (when did they last contact us? What other products do they hold with us?) – to enable a seamless experience which proves to the customer they are valued and understood.

Turning to technology

Looking ahead, AI will become increasingly important for banks when it comes to the customer journey. Many banks are already open to the possibilities of machine learning – and it has to be said, the capabilities of chatbots is becoming very impressive. Swedbank’s web assistant Nina, for example, now has an average of 30,000 conversations per month and can handle more than 350 different customer questions.

But the customer experience depends on both the quality of the data, and how well employees can use it to then bring insight to their interactions. In my opinion, customer-facing employees and technology should work side by side to enrich the customer experience. The role of chatbots, virtual reality, NLP and so on should be to bring efficiencies to business operations, particularly when it comes to automating tasks and processes where humans don’t add value. In fact, a recent report by Accenture found 79% of banking professionals agree that AI will revolutionise the way they gain information from and interact with customers.

If banks rise to the challenge to store and manage all their data together, and their employees are supported with the right training and technology to quickly access customer data and understand – and even pre-empt – their needs, they’ll be on the path to success.

Technology is bringing the finance industries one step closer to fighting money laundering thanks to the special identification of irregularities in trends and patterns of data, thus creating more 'hits' and fewer 'false negatives.' Aashu Virmani, CMO at Fuzzy Logix here talks to Finance Monthly about the potential impact data analytics can have on fighting money laundering and changing your business for the better.

As long ago as November 2009, Forrester published a research report entitled 'In-Database Analytics: The heart of the predictive enterprise'.  The report argued that progressive organisations 'are adopting an emerging practice known as 'in-database analytics' which supports more pervasive embedding of predictive models in business processes and mission-critical applications.’ And the reason for doing so?  'In-database analytics can help enterprises cut costs, speed development, and tighten governance on advanced analytics initiatives'.  Fast forward to today and you'd imagine that in-database analytics had cleaned up in the enterprise?  Well, while the market is definitely 'hot' it appears that many organisations have still to see the need to make a shift.

And that's despite the volumes of data increasing exponentially since Forrester wrote its report meaning that the potential rewards for implementing in-database analytics are now even higher.

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Given we can deliver our customers with analysis speeds of between 10 - 100 times faster than if they were to remove the data to a separate application outside of the database, we have a 'hard metric' that is very compelling in helping us convince prospects of the value of in-database analytics.  It's what gives us confidence that the shift to in-database analytics as the standard for data analysis is a question of time rather than choice.  Quite simply, the volumes of data that are increasingly being created mean that the only way to process the data and find analytical value is by doing so within the database.  But, as ever, real world examples are the best way to illustrate a point so let's take an unusual one; money laundering.

Banks have a vested interest in ensuring they stay compliant with the regulations in place for catching and reporting anti money laundering (AML).  The regulations have been in place for several years, and it is likely that most large banks have systems/processes in place to track and catch money-laundering activity.  Despite this, we still hear about cases where the authorities have fined reputable banks for their failure to implement proper AML solutions.  Not too long ago, in 2012, HSBC was fined $1.9 Billion by the US Department of Justice for “blatant failure” to implement AML controls related to drug trafficking money and, as recently as 2017, Deutsche bank was fined $650m by British and US authorities for allowing wealthy clients to move $10 billion out of Russia.  So why are current implementations/best practices not keeping up?

Let’s look at 3 big factors that contribute to compliance failure in the realm of anti-money laundering:

• The number of non-cash transactions around the world is exploding. Think about applications like Uber, Venmo, Paypal, Xoom and various international money transfer services (almost every large retailer is now in the Moneygram business) –  the number of non-cash transactions in the world has exploded.  The number crossed $426 billion in 2015, and is growing at over 10% annually.  Conclusion:  There’s just a lot more hay to find the needle in.
• Legacy solutions were largely rules-based. A program would run through the list of rules, and if any transaction matched a rule, a STR (suspicious transaction report) would be raised, meaning the transaction needed deeper analysis – often with a human in the loop.  These rules were good at some point in time, but there are several issues with any rules-based system:  (a) Rules are static, and require constant periodic refreshes.  For instance, is a $4000 deposit to a rural bank typical of a crop cycle, or an anomaly?  (b) Rules do not often transcend cultural/economic/geographical boundaries – e.g., a $400 hotel room in Washington D.C. may be normal, but in rural India it might warrant a second look.  Finally (c) Rules present an inherent bias and don’t look at the data to determine what is ‘normal’ and therefore what pops out as ‘abnormal.’
• The analysis of AML is mathematically complex. Incidence of fraud is a rare event, compared to the data that needs sifting through, and catching AML transactions involves looking deep into the history of the account.   Each time the system flags a transaction for a deeper look, a human has to investigate further.  False positives mean a lot of resource and time investment for the bank.  Couple this with the explosion in the number of transactions and the multiple channels through which non-cash transactions occur today, and you can see why systems that were put in place over a decade ago are in serious need of an upgrade.

With the money at stake for money launderers (according to the UN, $2 trillion is moved illegally each year), the efforts taken by criminals to avoid detection have become incredibly sophisticated.  Organised crime is continually seeking ways to ensure that the process of money laundering is lost within the huge amounts of financial data that are now being processed on a daily, hourly and even by-the-minute basis.  Their hope is that, because so much data is being processed, it is impossible to spot where illegal money laundering activity is happening.  And they'd be right, if you had to take the data out of the database for analysis.

Achieving a good degree of accuracy in a typical large bank means having to analyse billions of data points from multiple years of transactions in order to identify irregularities in trends and patterns. A traditional approach would require moving the data to a dedicated analytical engine, a process that could take hours or days or more depending on the volume of data. This makes it impossible to perform the analysis in a manner that can provide any real value to the organization. With in-database analytics, there is no need to move the data to a separate analytical engine, and the analysis can be performed on the entire dataset, ensuring the greatest possible coverage and accuracy.

One of our largest customers is a leading retail bank in India.  It was experiencing a rapid growth in data volumes that challenged its then-current AML processes.  By not needing to move the data for analysis, we were able to analyse billions of data points over a number of years (3+) of historical data to identify possible irregularities in trends/patterns, and do so in under 15 minutes – faster than any other method.  By not working to a pre-defined set of analytical rules and by letting the data 'speak for itself', it is possible to uncover patterns which occur naturally in the data. As a result, the bank is seeing an improvement of over 40% in terms of incremental identifications of suspicious activity and a 75% reduction in the incidence of 'false positives'.  In short, good guys 1, bad guys 0 because in-database analytics is having a very real impact on the bank's ability to spot where money laundering is happening.

I'm pretty sure that when Forrester published its report into in-database analytics towards the end of the last decade, it didn't envisage the fight to combat money laundering being a perfect case study for why in-database analytics is a no brainer when handling large volumes of data.  But in today's world, with ever increasing data volumes and the requirement to understand trends and insight from this data ever more urgent, in-database analytics has now come of age.  It's time for every organization to jump on board and make the shift; after all, if it can help defeat organized crime, imagine what it could do for the enterprise?