According to EveryCloud, cybercriminals netted $445 billion last year alone. What’s even more sobering is that 43% of cybercrimes target small businesses and their finances.

This is a worrying statistic for small businesses. All businesses take a hit if their data is breached, but larger businesses usually have a recovery plan in place. It can be a lot more difficult for smaller businesses to recover because of the costs associated with recovery.

That brings us to the point of this post – how you can protect your business from an attack.

Start with a Recovery Plan

It might seem as though we’re putting the cart in front of the horse here. That said, it’s better to plan ahead with something like this. Have a solid plan in place:

•         You want to ensure every device your employees use is protected – especially if they use their personal devices to access your system.
•         Consider using a cloud-based solution to keep your system safe. These programs provide real-time online protection. Another advantage is that you’ll also normally have an email scanner included. This means that dodgy emails are quarantined before your employees get a chance to open them.
•         Do regular backups.
•         Consider insurance. This will ensure that you have the money to recover from an attack.
•         Work out a fast recovery strategy. Who do you contact first? How do you proceed step by step in the case of a breach?

Train Your Employees

Human error is the hacker’s best friend. They’re just waiting for you or someone on your staff to make a mistake. Security awareness training conducted on a regular basis is your best defense. This training teaches you about the different threats, how to guard against them, and gives you the best practices to follow to keep your business safe.

Final Notes

If you want to mount the best defense against cybercriminals, adopting a multi-pronged, proactive approach is the best way forward. Start by securing your systems today.

Most sectors are having to comply with said rules and conform to industry trends, thus evolving based on the limitations regulations have imposed on them. According to Aravind Srimoolanathan, Senior Research Analyst - Aerospace, Defence & Security at Frost & Sullivan, this is particularly applicable in the biometrics sector, as it progresses in line with regulation presenting increasing opportunities for biometrics to excel in a security driven data world.

The Swedish data protection authorities (DPA) recently levied the first fine of approximately $20,000 to a high school which ran trials of facial recognition technology among a group of students to monitor their attendance. The school authorities argue that the program had the consent of the students, though that did not soften the stance of the regulator. The European data protection board citing the ‘imbalance’ between the data subject and the controller of data. Canvassing the multiple opinions floating on the web¹, Frost & Sullivan notes multiple cases of violations reported in Bulgaria and Austria post the incident in Sweden. The regulatory breaches have led to similar fines levied by the respective local data protection agencies tasked to enforce GDPR. Have the flood gates opened? Will this drown the Biometric market? Probably not, but it does raise significant concerns which need to be assessed and responded, to continue bringing the associated benefits of Biometric technologies to business and security operations.

General Data Protection Regulation (GDPR) is designed for the protection of personal data. GDPR emphasises on a person’s right to protect their personal data, irrespective of whether the data are processed within or outside the EU. Any data that could be linked to a person is subsumed into the definition of “personal data”. The regulation comprises of several articles and clauses which require compliance by all forms of agency - public, private or individual, that processes personal and sensitive data of clients, companies or other individuals. The regulations not only addresses data protection and privacy of individual citizens of European Union (EU) and European Economic Area (EEA) but also data transfer outside EU and EEA.

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In summary- data is expected to be stored, managed, and shared in an individual-centric approach rather than a collateral approach.

The challenges in managing identity in the modern world through conventional methods such as ID cards and PINs/ passwords are failing to address efficiency, accuracy and security requirements. The exponential demand for biometric-based ID management and access control systems drives the need to overcome such challenges. Biometric technologies (yes, facial recognition is one of them) curtail unauthorised physical and cyber access preventing identity fraud, enhance public safety, and drive seamless and efficient processes ensuring higher safety, convenience, and profits.

The Sweden High School case indicates the extent of GDPR is not just limited to giant corporations such as British Airways but also smaller public and private entities ‘mishandling’ data and hence violating the dictates of the GDPR regulations.

Frost & Sullivan’s collation of perspectives and insights from across the industry indicates that biometric technologies will replace conventional methods of Identity and Access Management in the years to come, not a case of if but when. Continued enforcement of data regulations would drive proper use case definition and regulatory compliance, but for this the suppliers and operators of these technologies need to create compliant secure by design solutions and processes. The first step is ensuring secure operations of the systems, and second is to design robust and verifiable processes for the associated data generated. Thirdly, defining the application of harvested data within the ethos of GDPR and related governance.

In the short-term though, with a surge in biometric technologies adoption, Frost & Sullivan anticipates we will witness an uptick in number of GDPR violation cases, due to partial and/or improper understanding of data privacy regulations. Though there is a risk that the hefty fines may slow down the pace of widespread adoption of biometric technologies, Frost & Sullivan proposed three-step strategy will drive healthy demand. Organisations that are digitally transforming their businesses for enhanced process efficiencies as part of their digital strategy would need to realign strategies to comply with general data protection regulations.

Biometric technologies are gaining infamous popularity with the data breaches, privacy concerns and unethical commercialisation of the associated data. GDPR, the Achilles heel as it may prove to be for the Biometric market, does not necessarily need to be – instead, the principles of GDPR can itself become the value proposition of the future biometric technologies.

1 http://www.enforcementtracker.com/

2 https://www.infosecurity-magazine.com/news/gdpr-spurs-700-increase-data/

In short, this means that in order to continue to be seen as a value-added service or department, finance professionals must evolve to keep up with ever-changing technologies.

Laura Timms product strategy manager at MHR Analytics explores  with Finance Monthly some of the biggest changes we can expect to see in the role over the coming years.

1. From mathematician to business consultant

Traditionally, finance professionals had to rely on historical, internal data to draw insights.

Not only was this data limited in scope, but it failed to give a full perspective of how decisions today would impact the future.

Now, the introduction of predictive analytics has helped moved finance professional’s analysis from asking “why did it happen?” to exploring “what will happen next?”.

Access to in-depth insights will enable finance professionals to track customer data in real-time and evolve from simply keeping of records, to carrying out in-depth analysis of the data.

Gone are the days of working discretely behind the scenes as the “number cruncher” of the business. The future of the role will increasingly see finance professionals using value-added analytics to position themselves as a strategic voice within a business.

Their unique visibility of the holistic position of the business will allow them to analyse and interpret anomalies and trends. This information can then be passed to the internal stakeholders to help them to make value-added decisions.

2. Remote working

The introduction of Cloud computing has taken the reigns off the finance professional.

Previously bound to the place of work or client’s offices, Cloud will work to exchange the cubicle lifestyle for more flexible working, with such roles able to be carried out anywhere.

The enhanced security of Cloud systems will allow finance professionals to unlock and share insights wherever they are, without having to worry about the traditional repercussions associated with handling sensitive data outside of the confines of the office.

Plus, a rise in businesses opting for a single online system, with all data in one place, creates simplicity without the need for multiple bulky applications.

Soon finance professionals will be able to share their analysis with their team at a click of a button and have a real-time view of what’s going on in their business whether they’re at home or on the go.

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3. Use of non-financial data

Financial data has long been the cornerstone of the finance professional’s work. It was from this data that patterns were spotted, reports were created and recommendations were made.

But the truth is that financial data alone only tells part of the story. As other types of data become more widely available, this will be increasingly used to further enrich financial insights.

Customer behaviour patterns can be used to detect fraud and suspicious activity, and supplier data can be used to anticipate shipment information so that this can be considered when creating forecasts. Finance professionals can even use internal data such as employee performance metrics to identify the ROI that each employee provides to the organisation, so that they can make recommendations accordingly.

Implementing such data into the review process works to improve top-line revenue and injects further value to financial insights. Research by FSN on planning, budgeting, and forecasting backs this up, with findings revealing that CFOs that make good use of non-financial data are able to forecast with 90-95% accuracy.

4. Highest ever standard of service

The rise of data analytics is facilitating an augmented workplace. In simple terms, we’ll see a rise in tasks that previously had to be completed by people, instead being carried out by machines.

Augmented analytics will allow much of the tedious administrative duties that have long been central to the finance professional’s role to be traded in for a more efficient way of working.

It will work to process data, bring it into context and lead in getting answers from it; giving more time for people to generate deeper insights for the business.

This technology will leverage finance professionals’ expertise, enabling them to focus on providing a higher quality service than ever. This will raise the bar in the industry, with businesses and clients alike recognising the direct impact that such roles have on their bottom-line.

5. Emergence of data-driven roles

The augmentation of traditional roles will see the emergence of data-driven alternatives to traditional bookkeeping and accounting roles.

As data becomes more and more central to the finance professional’s role, and as organisations become increasingly reliant on finance professional’s insights to drive their business strategy, the mutualistic relationship between finance and data will become ever more apparent.

In the near future, all finance professionals will be expected to have some knowledge of data analytics. But leading up to this, we’ll see the emergence of data science hybrid roles that will form out of businesses’ demand for data-savvy specialists.

This means seeking extra training to become proficient in data analytics sooner rather than later will help finance professionals stand out from the crowd and solidify their knowledge before this becomes a necessity.

References: 

https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-us-da-3minFinanceAnalytics.pdf

https://www.accaglobal.com/uk/en/member/member/accounting-business/2016/02/insights/data-analytics.html

https://blog.kenan-flagler.unc.edu/macwp-why-does-should-data-analytics-matter-to-accountants/

https://www.ey.com/en_gl/digital/how-analytics-can-help-transform-cfos-from-accountants-to-strate

https://www.accaglobal.com/ie/en/member/discover/events/global/e-learning/leadership-and-management/data-analytics-cpd-skills.html

https://careers.accaglobal.com/careers-advice/returners-to-work/finding-flexible-work/what-exactly-is-flexible-working.html

https://www.forbes.com/sites/workday/2017/08/23/why-non-financial-data-is-a-cfo-game-changer/#3a26278450b4

However, not all crime is conducted directly online. Some people are tricked into giving away details over the phone or are told to use their banking app to transfer money into a safe account. This multi-channel approach means that at every touchpoint, an organization must be aware that their customers could be at risk; they need to put systems and processes in place to mitigate cybercrime. 

According to a report by McAfee, the European economy is one of the worst affected areas in the world. The statistics suggest that 0.84% of Europe's GDP is affected. Looking at the UK specifically, it is estimated that the cost of cyber-crime to the UK economy is £27bn – and it is growing.

GDPR and Customer Data Breaches

One of the latest and most high-profile risks that have come to people's attention over the past 18 months are customer data breaches. Customers are increasingly aware that organizations hold a lot of their personal data and they want to be sure that it is safe. The General Data Protection Regulation was brought into place to ensure that organizations are acting responsibly when it comes to processing and storing customer data.

The financial impact of not following these guidelines, or for not having the correct systems in place, has been significant. Just months after the new regulation came into place, British Airways were one of the first companies to fall foul when 500,000 pieces of customer data were stolen, which resulted in them receiving a £183m fine.

The Financial Fallout of Cyber Crime

Before any cyber-crime has taken place, there is a significant cost to businesses that need to purchase software, implement new processes and training, and even employ new cybersecurity teams to deal with threats. For global organizations, there may also be a need to hire consultants to advise on what they need to do to keep themselves and their customers safe.

One of the consequences of cybercrime that will affect every business is the direct costs. This could be money lost by the business or by consumers. It could also be the loss of reputation to a brand. If a bank suffers a cyberattack and customers lose money, they are likely to lose confidence, which can have a huge knock-on impact on business performance and profits.

Following on from an attack, there may also be payments that need to be made. On top of losing money in an attack a business, may also need to pay out compensation, fines, and legal costs. Depending on the type and severity of the attack and the data that was lost, this can amount to millions of pounds, as demonstrated by the British Airways case.

By utilising high-quality and targeted data, you can be able to connect with more of the right individuals, getting more leads and reducing costs during the process. On the contrary, utilising the wrong data can result in dire consequences for your entire organisation other than your marketing campaign failing to gain traction.

As such, picking the right B2B data provider is imperative. You need to be sure that the partner you will be working with has the credentials and ability to provide the results you are after. Whether you want phone numbers, postal addresses, email addresses or a combination of all, you will only have peace of mind if you trust that your data provider really cares about your company.

That being said, here are important things to look at when picking a business data provider in the UK.

Verifiable Sources of Data

Can the provider tell you just how they garnered the data that they're selling? Also, have their sources been thoroughly inspected? If the answer is no, that should be a red flag. If you have proof that the business data is from a credible source, you'll want to check how often it's updated. Business data is constantly evolving and decays pretty fast. As such, the data needs to be cleaned and refreshed on a regular basis or you won't get the results you're after.

Proper Accreditations

Your business data provider needs to be registered with the Data Protection Act and the Information Commissioner's Office (ICO). Ideally, it is worth looking for a data provider that's registered with the Direct Marketing Association. This is a network of over 1000 firms that provides the best practice guidelines and legal updates. Each member is expected to collect data in an ethical manner.

While undertaking this process, it is a good idea to review the business data provider's own site in a more general manner. Do they have contact information like postal address and phone number? An unscrupulous provider may hide being their site, selling you data and then going missing thereafter.

Thorough and Targeted Data Records

What you deem as targeted and thorough will certainly depend on your specific needs. Regardless, it's best to have detailed information than the opposite. For instance, are you just given employees names, or are you told more about their roles? Also, can that data be paired up? For example, a postal address linked to an email address?

The best business data providers in UK will work closely with your to source data that best match your marketing and business goals. They will conduct penetration analysis or profiling which involves analysing your clients and looking for what they have in common as well as what drives them. This information is then used to get similar prospects from their database and thus help boost your sales.

Guarantees in Deliverability

It is also important that your business to business data provider can verify that your marketing message will reach the individuals you are targeting most of the time. Of course, a 100% deliverability guarantee is impossible as there are numerous variables that can impact the outcome. However, your business data provider should be able to show that your emails and direct mails will reach the intended prospects and that your phone calls will be answered by the right individuals majority of the time.

Business data is imperative in reaching prospects and boosting sales in this day and age. You want to ensure you are on the right side if you're going to use a business data provider. Use the tips above to ascertain such.

Trump vs. China

Back in 1930, the US introduced the Smoot-Hawley Tariff Act, which raised their already high tariffs, triggering a currency war and, as economists argue, exacerbating the Great Depression. With President Donald Trump’s threat to put 10% tariffs on the remaining $300 billion of Chinese imports that aren’t subject to his existing levies, sending markets tumbling from Asia to Europe, the question on everyone’s lips is: Is history about to repeat itself?

In August, in a bid to hit back against Trump’s administration, Beijing allowed the Chinese yuan to plummet past the symbolically important $7 mark. Economists suggest that this currency manipulation is China’s attempt to display dominance and gain the upper hand in the trade war between the two countries as devaluating its currency could help counteract the effects of US’s long list of tariffs on Chinese goods.

As protectionist actions escalate and US-China relations continue deteriorating, investors and markets have been growing increasingly concerned even though Trump has delayed the imposition of his new tariffs until December. A full-blown trade war wouldn’t be good news to anyone and could seriously weaken the global economy, as the IMF has warned, making the world “poorer and more dangerous place”. Both sides are expected to experience losses in economic welfare, while countries on the sidelines could experience collateral damage. Furthermore, if tariffs remain in place, losses in economic output would be permanent, as distorted price signals would prevent the specialisation that maximises global productivity. The one thing that’s certain, no matter how things pan out, is that there will be no winners in this war.

Economists suggest that this currency manipulation is China’s attempt to display dominance and gain the upper hand in the trade war between the two countries as devaluating its currency could help counteract the effects of US’s long list of tariffs on Chinese goods.

Cyberattacks & data fraud

Millions, if not billions, of people’s data has been affected by numerous data breaches in the past couple of years, whilst cyberattacks on both public and private businesses and institutions are becoming a more and more frequent occurrence. With the deepening integration of digital technologies into every aspect of our lives and the dependency we have on them, cybercrime is one of the greatest threats to every company in the world.

Cyberattacks are rapidly increasing in size, sophistication and cost, as cybercrime and data breaches can trigger extensive losses. In 2016, Cybersecurity Ventures predicted that cybercrime will cost the world $6 trillion annually by 2021, up from $3 trillion in 2015. According to them, ”this represents the greatest transfer of economic wealth in history, risks the incentives for innovation and investment, and will be more profitable than the global trade of all major illegal drugs combined”.

 Emerging Markets crisis

Since the early 1990s, emerging markets have been a key part of investors’ portfolios, as they have been offering strong returns and faster growth. However, global trade tensions, a stronger US dollar and rising interest rates have hit emerging markets hard. Still far from catching up with the developed world, many supposedly emerging markets are developing at a slower pace, which combined with the threat of a global trade war and higher borrowing costs on the rise, has made investors pull in their horns. Emerging markets are the ones feeling the strain and financial panic has been gripping some of the world’s developing economies.

With political instability, external imbalances and poor policymaking which has led to full-blown currency crises in the two nations, Turkey and Argentina have been at the centre of an emerging market sell-off last year. But they are not the only emerging economies faced with a currency crisis – according to the EIU, some economies which are already in the danger zone and could suffer from the same currency volatility include Brazil, Mexico and South Africa.

Still far from catching up with the developed world, many supposedly emerging markets are developing at a slower pace, which combined with the threat of a global trade war and higher borrowing costs on the rise, has made investors pull in their horns.

If the currency crises in Turkey and Argentina continue and develop into banking crises, analysts predict that investors could abandon emerging markets across the globe. “Market sentiment remains fragile, and pressure on emerging markets as a group could re-emerge if market risk appetite deteriorates further than we currently expect”, the EIU explains.

 Climate crisis

In recent months, the media is constantly flooded with reports on the horrifying environmental risks that the climate crisis the Earth is in the midst of poses, but we’re also only starting to come to grips with the potential economic effects that may come with it.

Despite the significant degrees of uncertainty, results of numerous analyses and research vary widely. A US government report from November 2018 raised the prospect that a warmer planet could mean a big hit to GDP. The Stern Review, presented to the British Government in 2006, suggests that this could happen because of climate-related costs such as dealing with increased extreme weather events and stresses to low-lying areas due to sea level rises. These could include the following scenarios:

• Decrease in agricultural areas crop yields due to hotter weather and draught.
• Rebuilding roads that have been destroyed by flooding caused by rising seas and more frequent hurricanes.
• Having to build more efficient power grids because the existing ones are not able to withstand extreme weather conditions.

Due to climate change, low-lying, flood-prone areas are currently at a high risk of becoming uninhabitable, or at least uninsurable. Numerous industries across numerous locations could cease to exist and the map of global agriculture is expected to shift. In an attempt to adapt, people might begin moving to areas which will be affected by a warmer climate in a more favourable way.

A US government report from November 2018 raised the prospect that a warmer planet could mean a big hit to GDP.

All in all, the economic implications of the greatest environmental threat humanity has ever faced range from massive shifts in geography, demographics and technology – with each one affecting the other.

Brexit

Fears that the UK could be on the brink of its first recession in 10 years have been growing after figures showed a 0.2% contraction in the country’s economy between April and June 2019. A weakening global economy and high levels of uncertainty mean the UK’s economic activity was already lagging, but the potential of a no-deal Brexit and the general uncertainty surrounding the UK’s departure from the EU, running down on stock built up before the original 29^th March departure date, falling foreign investment and car plant shutdowns have resulted in its GDP decreasing by 0.2% in Q2. This is the first fall in quarterly GDP the country has seen in six and a half years and as the new deadline (31^st October) approaches, economists are concerned that it could lead to a second successive quarter of negative growth – which is the dictionary definition of recession.

And whilst the implications of Brexit are mainly expected to be felt in the country itself, the whole Brexit process displays the risks that can come from economic and political fragmentation, illustrating what awaits in an increasingly fractured global economy, e.g. less efficient economic interactions, complicated cross-border financial flows and less resilience and agility. As Mohamed El-Erian explains: “in this context, costly self-insurance will come to replace some of the current system’s pooled-insurance mechanisms. And it will be much harder to maintain global norms and standards, let alone pursue international policy harmonisation and coordination”. Additionally, he goes on to note that tax and regulatory arbitrage are likely to become more common, whilst economy policymaking could become a tool for addressing national security concerns.

“Lastly, there will also be a change in how countries seek to structure their economies”, El-Erian continues. “In the past, Britain and other countries prided themselves as “small open economies” that could leverage their domestic advantages through shrewd and efficient links with Europe and the rest of the world. But now, being a large and relatively closed economy might start to seem more attractive. And for countries that do not have that option – such as smaller economies in east Asia – tightly knit regional blocs might provide a serviceable alternative.”

It seems only yesterday the Competition and Markets Authority (CMA) decreed that larger banks’ long-standing customer relationships impeded competition and innovation.

Open Banking has opened the door for third parties to access bank held account data as well as giving the ability to initiate payments from a customer’s bank. Features designed to allow new services to be delivered giving users enhanced financial services along with new, safe and secure ways to pay.

 Soon these opportunities will be reflected in the rest of Europe as all banks ready themselves for a September go-live date. So what can Europe learn from Open Banking in the UK?

State of the UK

It’s well versed that Open Banking has been slow to take off in the UK. Indeed, by the time the implementation date arrived only four of the UK's nine biggest banks were ready. Nonetheless, we are now seeing some signs of impressive applications powered by Open Banking setting the standard for Europe.

The biggest challenge in the UK was that the concept and technologies used were new, resulting in a number of iterations being required to deliver products that meet market needs.

A key differentiator in the UK has been the Government introducing the Open Banking Implementation Entity that sets and polices progress.

PwC has estimated £7.2 billion in revenue will be created by Open Banking by 2022.

The European Landscape

The European landscape looks quite different. With no equivalent regulatory or policing body and no specific government drive, we are anticipating considerable variation of standards from bank to bank. Lack of consistency in how Open Banking is deployed will slow adoption as the development of new services becomes more complex and users do not receive a common experience.

To address this there are groups such as STET in France and the Berlin Group working to define standards for implementing Open Banking. There is also pressure from various banking trade bodies such as DDK in Germany pushing for commonality in standards.

The development of standards by such groups will help to create consistency, yet it still begs the question as to who will enforce regulation and uphold financial institutions to the specified due dates?

Cooperation between the banks

Naturally, the scale of this European go-live is not as straightforward as the UK’s due to the number of banks involved. Yet, it has the potential to unlock financial services and technological innovations that could position Europe as one of the leading financial regions when it comes to Open Banking.

Indeed, PwC has estimated £7.2 billion in revenue will be created by Open Banking by 2022. European banks need to view this as an opportunity to enhance banking capabilities and deliver for increasingly tech-savvy consumers both within and cross country borders.

One lesson to be learnt from the UK is that embracing Open Banking allows banks and financial institutions to innovate and deliver exceptional services to their customers.

Embracing Open Banking

And what about the wider world? In Europe, there are two aspects of Open Banking, one covering access to data and the other dealing with payments. Adoption around the rest of the world is developing at a pace, with many countries either already living with viable applications or in the process of introducing legislation. Differing areas are focusing on specific aspects of Open Banking, for instance, Australia looks more to the data usage whereas India already has a successful payment infrastructure based on these principals.

Despite the local and regional nuances affecting markets yet to go live with Open Banking, one lesson to be learnt from the UK is that embracing Open Banking allows banks and financial institutions to innovate and deliver exceptional services to their customers. Open Banking requires banks to cooperate with others to deliver the desired objectives of innovation to meet the ever-changing needs of customers.

Then, and only then, will we witness an explosion of new products and services for consumers throughout Europe and realise the true benefits of Open Banking.

When you think about it, banking customers are leaving a trail of data when they conduct financial transactions – deposit activity, recurring payments, purchasing behaviours, borrowing activities and even when they just shop for financial services. All customer interactions – whether it is a point of sale, a tap on the screen, or a keystroke – generate insights on purchasing behaviour, clicks, searches, likes, posts and other valuable information.

Data usage has made an important difference in the changing landscape within financial services and the emergence of FinTech companies. Here in the UK, regulatory changes like PSD2 have created a new era of Open Banking where bank customer data will begin to flow amongst financial services providers. With this, the operating model for the traditional financial services companies is changing.

There are new entrant FinTech companies which have shown the ability to access and make sense of data in new and creative ways. Some of these start-ups are giving incumbents a run for their money not because they’re generating or accessing more data, but because they’re looking at it differently and using it in new ways. When FinTech companies get clarity about the use of data, make sense of it, organise and cleanse it, combine traditional and non-traditional sources, they can out-manoeuvre and out-innovate the incumbents.

There are three Vs which are fundamental to the management of data: volume, variety, and velocity.

There are three Vs which are fundamental to the management of data: volume, variety, and velocity. Given the increasingly competitive environment, evolving customer expectations, and regulatory constraints, financial services providers are seeking new ways to leverage data and technology to gain efficiency and a competitive advantage. The adoption of Big Data and new data management strategies is redefining the competitive landscape of financial services and companies that don’t have a strategy run the risk of losing market share.

To address this situation, financial services companies are investing in new and modern data management strategies that address both enterprise data and their Big Data assets. This new data environment must act at the speed of business, offering real-time insights that are created using massive volumes of data. New data-driven innovations include analytical tools such as machine learning and predictive analytics. These capabilities connect and leverage data across their entire enterprise and outside partners.

With all the changes taking place, there are many challenges and opportunities. Based on our experience working with many of the largest global financial services companies, we have observed a lot of focus and investment in these three following areas:

1. Creation of a Unified Financial Services Data Model.

This represents a standardised, multipurpose data model that creates a single, consistent view of the customer. This modern data environment is a business-driven data model that should serve all analytical requirements. It should also support all business domains such as marketing, risk management, product, customer experience, compliance, regulatory reporting, finance, and other functional areas.

It is critical that this environment is extensible and supports ongoing change. The activation of data that is stored must provide simple access for analytical applications as marketing, customer experience management, risk and other functions must respond in a real-time manner to create the desired customer experience or prevent fraud from occurring.

There are many other capabilities that can be delivered from this Unified Data environment. It is a foundational capability to address the rapid explosion of data, channels, devices, and applications.

2. While data collection is important, collecting more data is not always the answer. Ingesting the best sources and continuously testing them for accuracy and predictive capabilities is critical. New alternative sources of data are being created every day. While some of these sources can create some unique value, other sources may only add complexity to data management and cost without the desired return.

Deep mining of data can help predict needs and enable a much-improved customer experience. Improving the quality and accuracy of data that is collected, stored in the cloud, processed and analysed by artificial intelligence and deployed is important when creating new targeted offers and enhancing a customer experience.

Diligence in the areas of consumer privacy and security is and will continue to be paramount.

3. Diligence in the areas of consumer privacy and security is and will continue to be paramount. Consumer understanding of how their data is used often lags behind the pace of innovation, inspiring new demands from government agencies and consumer advocacy groups around the world. These factors compound the liability every financial services company faces when managing and activating consumer data.

Data security and privacy is an important issue and historically has been a strong point of differentiation for financial services companies, especially in light of the continued discussion around how Facebook and other social media companies manage data. There is and will always be an expectation that financial services companies remain a trusted guardian of data.

As financial services leaders realise that more trusted, connected and intelligent data contributes to their competitive position and survival, they now see data as an essential asset. This asset also requires investment to unlock value. Data should not be looked at as a driver of costs, but an important asset that will pay off handsomely for tomorrow’s financial services leaders.

About Scott Woepke

Scott Woepke is Head of Financial Services Strategy at global data, marketing and technology company Acxiom, where he leads a global team. He has over 30 years of hands-on experience in many facets of marketing, distribution, product, and technology strategy in the financial services and FinTech industries. His work includes working with many of the world’s largest financial services companies across retail/consumer banking, credit cards, investment services and payments.

 Website: https://www.acxiom.co.uk/

F-Secure’s Cyber ‘Threat Landscape for the Finance Sector’ shows that the sophistication of adversaries targeting banks, insurance companies, assets managers and similar organizations can range from common script-kiddies to organized criminals and state-sponsored actors. And these attackers have an equally diverse set of motivations for their actions, with many seeing the finance sector as a tempting target due to its importance in national economies.

The report breaks down these motivations into three groups: data theft, data integrity and sabotage, and direct financial theft.

“This is a useful way to think about cyber threats, because it is easy to map attacker motivations across to specific businesses, and subsequently understand to what extent they apply,” says F-Secure Senior Research Analyst George Michael. “Once you understand why various threat actors might target you, then you can more accurately measure your cyber risk, and implement appropriate mitigations.”

Data integrity and sabotage – where systems are tampered with, disrupted or destroyed – is the cyber criminals’ method of choice. Ransomware and distributed denial-of-service attacks (DDoS) are among the more popular techniques used by cyber criminals to perform these attacks.

Similar attacks have been launched by state-sponsored actors in the past. But these are less common and often linked to geopolitical provocations such as public condemnation of foreign regimes, sanctions, or outright warfare.

And while North Korea has the unique distinction of being the only nation-state believed to be responsible for acts of direct financial theft, their tactics, techniques, and procedures (TTPs) have spread to other threat actors.

According to Michael, this is part of larger trend that involves adversaries offering their customizable malware strains or services-for-hire on the dark web, contributing to a rise in the adoption of more modern TTPs by attackers.

“North Korea has been publicly implicated in financially-motivated attacks in over 30 countries within the last three years, so this isn’t really new information,” says Michael, “But their tactics are also being used by cyber criminals, particularly against banks. This is symbolic of a wider trend that we’ve seen in which there is an increasing overlap in the techniques used by state-sponsored groups and cyber criminals.”

In addition, understanding cyber threats relevant to specific organizations is crucial to being able to detect and respond to an attack when it occurs.

“Understanding the threat landscape is expensive and time-consuming,” says Michael. “If you don’t understand the threats to your business, you don’t stand a chance at defending yourself properly. Blindly throwing money at the problem doesn’t solve it either – we continue to see companies suffer from unsophisticated breaches despite having spent millions on security.”

As a result Jason Lin, CFO at Centage Corporation says CFOs are losing sleep over the end result. This is so far from ideal, which is why I’m offering these five recommendations to help financial teams sleep better.

1. Instill confidence in your data

I totally get why finance teams lack confidence in their budget data. Last year’s actuals are typically re-keyed into a budget spreadsheet, and manual data entry inevitably leads to mistakes. Worse, it’s incredibly difficult to spot where, in a series of spreadsheets linked together with macros, a zero may have been left out or numbers were transposed. And once the data is entered, it’s used for what-if scenario planning -- i.e. predicting the future -- which takes the budget even further away from the “truth.”

Finance teams can get a lot more sleep if they ditched the spreadsheet and replaced it with a tool that can pull data directly from their GLs. Not only will the data be accurate (and teams spared countless hours of data entry), the budget will be a replica of how the business is organized, making scenario planning a lot more accurate. Of course, the predictions may still be wrong, but at least the effects of those assumptions on the financial statements will be realistic.

2. Avoid forecasts that have major variances versus actuals

This is a tough one because there are so many external variables that can affect the actuals. What will the economy do? Will interest rates go up? Will new tariffs drive up manufacturing costs? How is that upcoming election going to shake out? In all honesty, attempting to predict market conditions in Q4 2020 in the summer of 2019 is a bit unrealistic. No amount of effort will change that reality.

My best recommendation: move to a rolling forecast that’s updated monthly, or at least once a quarter. Not only will it lessen the variances, but it will also allow teams to spot trends that have the potential to affect the goals set (positively or negatively) much earlier.

3. Test your assumptions for accuracy

I realize what a big ask this recommendation is. This issue of testing your assumptions for accuracy will never go away because, as mentioned above, there are way too many factors that affect performance but are way outside of your control.

While you can’t control what will happen, you can anticipate potential variances and put plans in place to respond to them. Scenario planning and what-if scenarios are your saving grace here. For instance, you can test the impact on your P&L if sales decrease by, say 10%, or if the cost of oil spikes. You might not like what you see, but at least you’ll know ahead of time the potential outcomes so you can warn the executive team upfront, and make contingency plans if your assumptions aren’t correct.

4. Meet your budget deadlines and be boardroom ready

When I hear the concerns of CFOs about meeting deadlines I like to tell people what Steve Player, noted business author and Program Director for the Beyond Budgeting Round Table (BBRT) North America, has to say about it. To paraphrase his viewpoint: starting earlier is a terrific way to build more errors and delays into your budget. Again, in the summer of 2019 you are attempting to predict what Q4 2020 will look like. Do you know the outcome of the 2020 election? Do you know whether we’ll continue to see massive flooding in the South and Midwest? How will either of these events affect your actuals?

The solution is to shift your focus to a continuous process. If you believe in planning, why not do it monthly? It makes no sense whatsoever to start earlier and earlier when it’s not humanly possible to predict what the world will look like 18 months from now.

5. Break down your company silos

It shouldn’t come as any surprise that when budgets are created in silos, they won’t mesh with one another. Marketing will spend the summer months coming up with campaigns to launch the following year, while sales will review their customer and prospect pipeline and make their own plans. There is no connection between the two.

Financial teams have two options to address the issue of silos. First, implement a collaborative budgeting tool so that teams can see how their plans affect one another. If sales is pinning a revenue number of an increase in new SMB logos, marketing needs to know that, and to allocate part of their budget for an SMB customer acquisition campaign. Second, view this as an excellent opportunity to take a more leadership, hands-on role in the business. Bring the two teams together, and help them to create a tighter plan.

I realize that some of these suggestions can seem blasphemous; finance teams have always created budgets, stayed in the back office, and put stakes in the ground in terms of assumptions. But given the pace of business change, the old ways aren’t cutting it anymore. These tips reflect the reality of business planning today.

This week Finance Monthly hears from Caroline Hermon, Head of Adoption of Artificial Intelligence and Machine Learning at SAS UK & Ireland, on the adoption of open source analytics in the finance sector and beyond.

Open source software used to be treated almost as a joke in the financial services sector. If you wanted to build a new system, you bought tried and tested, enterprise-grade software from a large, reputable vendor. You didn’t gamble with your customers’ trust by adopting tools written by small groups of independent programmers. Especially with no formal support contracts and no guarantees that they would continue to be maintained in the future.

Fast-forward to today, and the received wisdom seems to have turned on its head. Why invest in expensive proprietary software when you can use an open source equivalent for free? Why wait months for the official release of a new feature when you can edit the source code and add it yourself? And why lock yourself into a vendor relationship when you can create your own version of the tool and control your own destiny?

Enthusiasm for open source software is especially prevalent in business domains where innovation is the top priority. Data science is probably the most notable example. In recent years, open source languages such as R and Python have built an increasingly dominant position in the spheres of artificial intelligence and machine learning.

As a result, open source is now firmly on the agenda for decision makers at the world’s leading financial institutions. The thinking is that to drive digital transformation, their businesses need real-time insight. To gain that insight, they need AI. And to deliver AI, they need to be able to harness open source tools.

The open source trend encompasses more than just the IT department. It’s spreading to the front office too. Notably, Barclays recently revealed that it is pushing all its equities traders to learn Python. At SAS, we’ve seen numerous examples of similar initiatives across banking domains from risk management to customer intelligence. For example, we’re seeing many of our clients building their models in R rather than using traditional proprietary languages.

A fool’s paradise?

However, despite its current popularity, the open source software model is not a panacea. Banks should still have legitimate concerns about support, governance and traceability.

The code of an open source project may be available for anyone to review. But tracing the complex web of dependencies between packages can quickly become extremely complex. This poses significant risks for any financial institution that wants to build on open source software.

Essentially, if you build a credit risk model or a customer analytics application that depends on an open source package, your systems also depend on all the dependencies of that package. Each of those dependencies may be maintained by a different individual or group of developers. If they make changes to their package, and those changes introduce a bug, or break compatibility with a package further up the dependency tree, or include malicious code, there could be an impact on the functionality or integrity of your model or application.

As a result, when a bank opts for an open source approach, it either needs to put trust in a lot of people or spend a lot of time reviewing, testing and auditing changes in each package before it puts any new code into production. This can be a very significant trade-off compared to the safety of a well-tested enterprise solution from a trusted vendor. Especially because banking is a highly regulated industry, and the penalties for running insecure or noncompliant systems in production are significant.

What use is power without control?

When it comes to enterprise-scale deployment, open source analytics software also often poses governance problems of a different kind for banks.

Open source projects are typically tightly focused on solving a specific set of problems. Each project is a powerful tool designed for a specific purpose: manipulating and refining large data sets, visualising data, designing machine learning models, running distributed calculations on a cluster of servers, and so on.

This “do one thing well” philosophy aids rapid development and innovation. But it also puts the responsibility on the end user – in this case, the bank – to integrate different tools into a controlled, secure and transparent workflow.

As a result, unless banks are prepared to invest in building a robust end-to-end data science platform from the ground up, they can easily end up with a tangled string of cobbled-together tools, with manual processes filling the gaps.

This quickly becomes a nightmare when banks try to move models into production because it is almost impossible to provide the levels of traceability and auditability that regulators expect.

Language doesn’t matter

The good news is that there’s a way for banks to benefit from the key advantages of open source analytics software – its flexibility and rapid innovation – without exposing themselves to unnecessary governance-related risks.

The language a bank’s data scientists choose to write their code in shouldn’t matter. By making a clean logical separation between model design and production deployment, banks can exploit all the benefits of the latest AI tools and frameworks. At the same time, they can keep their business-critical systems under tight control.

SAS plus open source

One SAS client, a large financial services provider in the UK, recently took this exact approach. The client uses open source languages to develop machine learning models for more accurate pricing. Then it uses the SAS Platform to train and deploy models into full-scale production. As a result, model training times dropped from over an hour to just two and a half minutes. And the company now has a complete audit trail for model deployment and governance. Crucially, the ability to innovate by moving from traditional regression models to a more accurate machine learning-based approach is estimated to deliver up to £16 million in financial benefits over the next three years.

Digital transformation: from buzzword to reality

Business’ biggest buzzword, digital transformation, has taken the technology world by storm. Despite being around since the 1990s, it has recently become a term that permeates almost every business strategy or vision for the future. Today, every technology provider claims it can enable digital transformation, and all well-informed CEOs are mandating it to drive their business forward.

The key to unlocking this business growth starts with technology’s second-biggest buzzword: data.

Barriers to a data-driven digital transformation

While data may be ‘the new oil’, an uncontrollable explosion of unrefined data doesn’t add any value to a business. You must be able to sort, process and examine data, view it from different angles and understand how to extract intelligent insights from it. To do this, having the right infrastructure in place is key. Indeed, large businesses in particular often struggle with legacy systems that aren’t designed to handle the volume of data we now produce and consume.

For finance departments, reliance on outdated technology is certainly part of the problem, but there are also other issues that need to be addressed. A recent BlackLine survey examining attitudes to financial data revealed that the C-suite’s top perceived challenge was that data was from too many sources and there was uncertainty over whether it was all being accounted for. Over a quarter of C-suite executives and finance professionals (28%) claimed that there were not enough automated controls and checks for the volume of data they had to deal with and that the process of collecting and processing the data was too complex (also 28%).

The key to unlocking this business growth starts with technology’s second-biggest buzzword: data.

So, what steps can finance professionals take to address these challenges? And what questions should finance departments ask as part of their quest to become truly data-driven?

Asses your data foundation

The vital first step for any transformation journey is to assess how far along the road you’ve already travelled. It might seem obvious that real-time access to accurate, reliable data – including financial data – can be used for strategic analysis and to create a competitive edge. But what may be less obvious is how damaging it can be to base this analysis on poor quality, unstructured or untrustworthy data.

According to BlackLine’s survey results, almost seven in 10 respondents believed that either they themselves or their CEO had made a significant business decision based on out-of-date or incorrect financial data. Not only can tapping into poor data compromise the decisions you make, but it can also seriously hamper your organisation’s ability to transform longer-term.

Is your data accurate?

With this in mind, the first question any organisation or department should consider is whether the data they do have is accurate. Is there real confidence in the precision of your data; can you be confident in the decisions you make from it? If there are inaccuracies, where are they coming from, and what processes or controls can you put in place to improve this?

The first question any organisation or department should consider is whether the data they do have is accurate.

In the finance department, clunky spreadsheets and outdated processes often leave finance teams in the dark until month-end, resulting in rushed work, manual workarounds and an increased risk of human error. By automating manual, predicable and repeatable processes, such as transaction matching or journal entries, data not only becomes more reliable but time is also freed up for more valuable work.

Is your data expansive and up-to-date?

Once you are comfortable that manual tasks have been automated, and are confident in the data being used to drive decisions, the next thing to consider is whether your data sets are expansive enough for intelligent analysis. Having a foundation of clean, relevant data is fantastic, but there must be enough of it to reliably answer pressing business questions.

At the same time, it’s vital to examine whether the data you do have access to is actually up-to-date. After all, why use data that is a month old to make decisions now? Continuous accounting, for example, which shifts the finance department from a monthly to a near real-time data cycle, makes it easier to deliver forward-looking, strategic insights that benefit the rest of the business.

Is today’s data ready for tomorrow’s demands?

Finally, finance departments need to ask whether the data foundations they are building today will be fit for purpose tomorrow. Much of this comes down to a question of trust: do you trust the processes you have in place to deliver data that is accurate, reliable, scalable and usable – not only now, but also in the long run?

Finance departments are bracing themselves for further technological disruption. A lack of trust in your data today not only has implications for human decision making, but it could also impact technology that learns from this data further down the line. While truly intelligent technology, like AI, may not be a reality for the finance department just yet, establishing its data integrity now will ensure it is ready for these advances when they do arise.