Data should be one of your strongest assets, not a confusing uncertainty or a burden to work with. Alastair Luff of global information services group Experian here talks about how you can make the most of the data you gather and use it for e key decision making in your operations.

Big Data has become a buzzword in the Financial Services industry. Put simply, it’s about businesses having an amount of data so large, it becomes difficult to digest and define a clear strategy.

Information is created every second of the day, and its complexity is advancing as new data comes onto the scene. The volume of data is growing significantly, presenting a notable challenge to businesses. On its own, data isn’t valuable – it’s the business insights it provides which makes it a vital asset. The more information, the greater the insight, and the bigger the opportunity to drive optimum outcomes.

Data – a confusion or a complement?

Data can seem daunting. It needs to be controlled, understood and used to avoid hindering compliance, and to create real value. It can also confuse the customer – with less than 8% understanding how their data is being used within organisations.

But it can also complement. Organisations are not only faced with external data sources, but also first party data generated internally. But two data streams doesn’t result in a complete customer profile, and in some situations, information captured over an extended period of time may become outdated. Overlaying current and validated data, such as credit bureau data, can add a layer of insight that fills gaps and helps complete a fuller picture.

The more comprehensive view available, the better lenders can tailor credit risk policies to ensure financially inclusive lending strategies that consider all relevant data assets, e.g. within credit scoring.

Scoring with the customer

Credit scoring is nothing new, but it’s not just about banks and lenders. Industries outside of finance are beginning to recognise its benefits and scoring is offering enhanced outcomes for customer engagement and enhanced credit risk provisioning. In Africa, for example, data from mobile phone usage is helping with credit scoring where no financial services data exists, giving more people access to credit.

While scoring itself is well established, the process behind it has evolved. Organisations, lenders especially, are approaching scoring differently, considering individual risk strategies, profiling and in some instances different data assets. All of these factors, whether standard or bespoke, can provide an automated risk assessment that identifies the credit strategy of an individual.

Simplifying complex information

The ability to make responsible lending decisions comes down to how well information is interpreted. This is where scorecards come in to their own. They can help rationalise complex insights and automate decision making. Businesses who overlay internal insight into scoring, with enriched external insight achieve a more comprehensive view of each customer’s credit history.

In an era confused by a mass of information, a more demanding customer and pressure on minimising loss, businesses need to understand the value and opportunity – but balance both. This extends beyond scoring as an action, and therefore it would be prudent businesses automate this area – using available insight to free up resource to support developments across other business areas which aren’t so easily resolved.

Using comprehensive scoring can provide advanced data feeds that contain varying benefits for the organisation, for example:

1. Understanding affordability. What does the future financial health of an individual look like? Are they likely to experience problems?
2. Geographical insight. Some people have little or no bureau data. Using geographical analysis can provide a view of how the region and area is trending to support any credit review.
3. Considering circumstance. Data for those with limited data, for example people living at home with their parents, can have their profile enhanced by overlaying relevant data. In addition lenders can consider the financial status of an individual, or an associate who they are linked to financially. This can provide a rounded view of any associations and identify any causes for concern.
4. Ensuring the person is genuine. Fraud is on the rise and using data to assess and identify the genuine intent of a person can be critical to losses and also protect customers.
5. Understanding how a person behaves. Behavioural data can provide rich insight into a person’s financial behaviours. From cash advances on credit, to limit vs. spend assessments. This can be particularly helpful in understanding a person’s financial trends and providing a prediction into their probable future trends.

Differing and advanced data assets can be used to on-board, and when a customer is on-boarded. It can be particularly useful during the lifetime of a loan in order to understand better any potential alignment to a business’s growth strategy.

In a world of Big Data, organisations have the opportunity to translate information into a currency. Understanding what insight it can bring, embedding it within credit risk and scoring policies can ensure accurate assessments and appropriate lending. Businesses just need to understand what data provides what – and why.

Forget about high-tech espionage. Many of the headline-grabbing hacks from the past few months hinged on low-tech social engineering—the use of deception to manipulate users into giving up their passwords and other data, writes LeClairRyan attorney David Z. Seide in a new post on the national law firm's "Information Counts" blog.

"This kind of hack takes many forms—examples include security alerts from what appear to be trusted websites to update passwords, and phishing emails from what appear to be known, trusted contacts asking to download files or click on provided links," writes Seide, a partner on LeClairRyan's Compliance, Investigations and White Collar team, based in the national law firm's Alexandria, Va., and Washington offices.

In the Feb. 27 post ("Cyber Security and Social Engineering: A Big Low Tech Problem"), Seide notes that the consequences of computer network penetration through social engineering have been dire for victims. He cites a prime example: the hack of Hillary Clinton's 2016 presidential campaign.

"There, the campaign chair received what appeared to be a genuine email from Google's 'Gmail Team' informing him that a Ukrainian computer had just used his password to try to sign in to his Gmail account," Seide explains in the piece. "The email went on to say that Google had stopped the attempt, advised the chair to change his password immediately, and provided a 'Change Password' link. Believing the email to be authentic, the chair clicked on the link and changed his password."

As the world now knows, of course, the new password went straight to hackers, who promptly downloaded 30,000-plus emails in the account and sent them to WikiLeaks for publication. "This hack succeeded only because hackers used social engineering techniques to trick the unwitting user into effectively giving a secure password to what appeared to be a trusted source," writes Seide, an experienced litigator and internal investigator, who led multiple high-profile internal and financial investigations for several federal agencies prior to joining LeClairRyan last month. Those roles included leading the Department of State Office of Inspector General team that reviewed and published multiple reports in 2016 concerning the use of personal email for official business by Hillary Clinton and four other Secretaries of State.

For the foreseeable future, he notes, low-tech social engineering hacking will continue to be a dominant cyber risk. "If anything, it is likely to proliferate across growing and emerging technology platforms—mobile and other Internet-enabled devices (Internet of Things) and social media," he explains.

This is precisely why defending against such hacks requires more and better "cyber hygiene," which Seide describes as "no different than regularly washing hands to prevent infection." Toward that end, he offers a set of best practices for guarding against social engineering. They include ramping up education about social engineering; closely monitoring the level of security-protocol compliance within your organizations; maintaining vigilance and skepticism, and engaging in timely reporting of hacks or potential hacks.

"Cyber security is an ongoing process that changes as fast as technology changes. And technology changes fast," the attorney writes in the conclusion to the piece. "These suggestions are by no means cure-alls. But they will reduce social engineering risk and may demonstrate a prudent effort to address a serious problem we all regularly face."

(Source: LeClairRyan)

Adaptive Insights has released its most recent  global CFO Indicator report, taking a closer look at the reporting process and how CFOs can free their teams to deliver the value-added analysis desired by key corporate stakeholders. Alarmingly, CFOs report that their teams continue to spend very little time on strategic tasks—just 17%—and remain reliant on the standard processes and technologies that negatively impact their ability to deliver actionable information.

The CFO Indicator Q4 2016 report reveals that while 85% of CFOs say their teams have direct access to the financial and operational data needed to generate accurate reports, it is the non-value-added tasks—like data gathering, verifying accuracy, and formatting reports—that take time away from the strategic analysis desired by top management and other stakeholders. Most CFOs also cite data integration as the biggest technology hurdle to gaining actionable reporting information, given the increasing need to report on both financial and operational data typically housed in disparate, unconnected systems.

“Our survey validated the ongoing challenges CFOs face today—the need to provide greater strategic value while balancing the increasing volume and sources of data,” said Robert S. Hull, founder and chairman at Adaptive Insights. “Reporting efficiency plays a critical role here as CFOs want their teams to spend more time on strategic tasks yet recognise both the technology and process challenges associated with today’s reporting activities—namely, time-consuming, error-prone manual data aggregation. CFOs must address these challenges now if they expect to fulfill their roles as strategic partner to company management teams.”

The key findings in the report show that:

•  Only 17% of the finance team’s time is dedicated to strategic activities. This number is down from the CFO Indicator Q2 2016 survey when the question was first asked and shows no progress for CFOs who also said they had a goal to double this activity by 2020.
  •    60% of CFOs cite data integration as the primary technology hurdle standing in the way of gaining actionable information for reports, necessitating manual data aggregation that eats up time and leads to inaccuracies.
  •    68% of CFOs expect a continued rise in report volumes. This rise will only serve to magnify process shortcomings, requiring CFOs to reassess their reporting platforms.

Manual data aggregation eats up time, causes errors
This quarter’s report shows more than half of CFOs (54%) say they generate reports by exporting data out of their ERP systems and into a Microsoft Office® application such as Microsoft Excel®, Microsoft Word®, or Microsoft PowerPoint®. Of those that report an inefficient process, 64% take this approach. For those who generate their reports directly out of their ERP system (21%), 41% periodically found their numbers to be inconsistent from report to report.

Because the lack of a centralised reporting system introduces inconsistencies in metrics, data, and calculations, finance teams must spend an inordinate amount of time verifying the accuracy of their reports. The report advises that to mitigate risk and save valuable resources, CFOs will need to solve the data integration issues standing in the way of gaining actionable information.

(Source: Adaptive Insights)

With the implementation of GDPR on our doorstep, companies risk serious vulnerability in the face of data protection. This week Finance Monthly has heard from Rafi Azim-Khan and Steven Farmer of Pillsbury Law, who gave us a rundown on how you need to prepare for the regulatory changes.

From the debate about the UK’s ‘Snooper’s Charter’, to a number of high-profile cyber-attacks and the wrangling, both legal and political, over the abolition of the EU-US data sharing treaty, Safe Harbour, data privacy has remained firmly in the media spotlight in recent months.

Following the most significant overhaul of the EU data protection regulations in recent years set to come into effect with the introduction of the EU General Data Protection Regulation (GDPR) in May 2018, this trend looks set to continue.

The GDPR rips up the existing legal framework and provides for the imposition of heavy fines. Equally seismic is the fact that the new rules have an extra-territorial reach, catching companies who traditionally did not need to prioritise data protection laws.

Significantly, however, few businesses are reported to have actually looked at what they need to do to ensure compliance under the GDPR. As the time until enforcement dwindles, it is essential that firms act, as the UK data protection regulator has said herself. So what do companies actually need to be aware of?

The letter of the law

The GDPR replaces the current EU Data Protection Directive 95/46/EC. As a Regulation, and unlike the old law, the new laws will be directly applicable in all EU member states.

Specific changes introduced include the following:

• Accountability – crucially, those caught will be required to show compliance e.g. (i) maintain certain documents; (ii) carry out Privacy Impact Assessments; (iii) implement Privacy by Design and Default (in all activities), requiring a fair amount of upfront work.

• Data protection officers (DPOs) – in many circumstances, those caught by the GDPR will also need to appoint DPOs and so thought will need to be given as to whether this applies and, if so, who that person or persons might be.

• Consent – new rules are also introduced relating to the collection of data, e.g., consent must be “explicit” for certain categories. Existing consents may no longer therefore be valid and consents obtained should be purged going forward.

• Enhanced rights for individuals – new rights are introduced around (i) subject access; (ii) objecting to processing; (iii) data portability; and (iv) objecting to profiling, amongst others.

• Privacy policies – fair processing notices now need to be more detailed, e.g., new information needs to be given about these new enhanced rights for individuals. Policies will need updating therefore.

• International transfers – Binding Corporate Rules for controllers and processors as a means of legitimising transfers are expressly recognized for the first time and so should be considered as a transfer mechanism.

• Breach notification – new rules requiring breach reporting within 72 hours (subject to conditions) are introduced and so processes in place (or not) will need to be revisited to accommodate these rules.

Of course, with the UK set to leave the European Union, there is much ongoing discussion about what the post-Brexit regulatory regime may look like. It is generally accepted, however, that after the UK leaves the EU, UK laws will nevertheless track the GDPR (e.g. via some form of implementing legislation or a new UK law which effectively mirrors the GDPR). In other words, even if you are purely a UK company, or you are outside the UK and targeting UK consumers only, you should not ignore these changes on the basis Brexit is some sort of get out of jail free card.

Who needs to comply?

All organisations operating in the EU will be caught by the new rules. Importantly, organisations outside the EU, like US-based companies that target consumers in the EU, monitor EU citizens or offer goods or services to EU consumers (even if for free), will also have to comply.

The GDPR also applies to “controllers” and “processors”. What this means, in summary, is that those currently subject to EU data protection laws will almost certainly be subject to the GDPR and processors (traditionally not subject) will also have significantly more legal liability under the GDPR than was the case under the prior Directive.

What can businesses do to prepare?

To ensure compliance, companies need to ensure that they have robust policies, procedures and processes in place. With the risk of heavy fines under the GDPR, not to mention the reputational damage and potential loss of consumer confidence caused by non-compliance, nothing should be left to chance. In terms of key first steps, companies might consider prioritising the following as a minimum:

• Review privacy notices and policies – ensure these are GDPR compliant. Do they provide for the new rights individuals have?

• Prepare/update the data security breach plan – to ensure new rules can be met if needed.

• Audit your consents – are you lawfully processing data? Will you be permitted to continue processing data under the GDPR?

• Set up an accountability framework – e.g., monitor processes, procedures, train staff.

• Appoint a DPO where required.

• Consider if you have new obligations as a processor – is your contractual documentation adequate? Review contracts and consider what changes will be required.

• Audit your international transfers – do you have a lawful basis to transfer data?

As May 2018 draws inexorably closer, companies need to start thinking about compliance before it is too late to avoid being made an example of. As the old adage goes: those who fail to prepare, prepare to fail.

You may have heard the words ‘data management’ flying around left, right and centre with no clear understanding on what it is and how paying attention to said meaning could be useful to you, so this month Finance Monthly heard from Maysam Rizvi, a 15-year banking innovator, who provides particular insight into exactly why the data revolution is worth paying attention to. Maysam is the Founder and MD of Aelm, and is responsible for managing change initiatives at international institutions including J.P. Morgan and National Bank of Dubai.

In 2006, UK mathematician Clive Humby coined a phrase that was utterly obvious, hugely prophetic and unerringly timeless. Pointing at the raw material with which we'll build life's next phase, he said: “Data is the new oil.”

In 2017, some 2.5 quintillion bytes of data are created each day. At this rate, it'll take just three months to double the world's entire existing data stock. So Humby's statement is truer now than it was then: data is every industry's imperative. And that's quintuply true for banking.

If financial institutions want to edge ahead, and stay there, it's time to fully embrace data and its possibilities for the long term.

Financial institutions have been longsighted enough to harvest data, but our putting it to work has been sporadic and disorganised. We've been slow to deploy data in areas like regulation and compliance, and we've probably been over keen, and under-effective, in areas like credit and risk.

To digress slightly, I grew up watching movies like Terminator 2: classic struggles depicting robots (bad) versus humans (good). As a young man, I learned – as many of us did – not to trust a world that's in the hands of Artificial Intelligence (AI).

Whenever machines edge out a human workforce, or Hollywood spawns a new cyber villain, robots' reputations nosedive. But it's important to remember that AI is simply a manifestation of data: sets of numbers, trends and analytics built and programmed to perform tasks.

It's daunting, but today's data is the foundation of tomorrow's AI. And the effectiveness of banks' AI will, as the future of finance draws nearer, separate the wheat from the chaff.

The proposition is this: banking will soon rely incalculably on AI. The bedrock of AI is data. We are in a position to mine and manage rich data now.

If the story of the industrial revolution is one of optimising processes and stripping out costs, the tech revolution has utterly multiplied that paradigm.

Twenty years ago, cars started to, basically, build themselves along production lines. Today, quantum data and real-time machine learning means cars can now drive themselves. That's data in action.

And so is this: a 2013 study by Oxford University’s Carl Frey and Michael Osborne estimates that 47 percent of US jobs may be replaced by robots and automated technology within 20 years. Owing to all the brains required, banking is the kind of high cost industry where an AI coup is inevitable.

Since the ATM, we've given pieces of banking over to machines. From internet banking to intricate trading algorithms, anything that can be handed over to machines has been – and will be.

So, that's the proposition. And we can probably make peace with it. Then comes the practical.

How can banks adapt and ensure a steady transition?

On that, there's no quick answer. Whether it's retail or investment banking, preparing for mass AI means dramatically improving technology infrastructures, and sorting a lot of data.

Aside from what already sits in banks' data vaults – and what data is being crunched this very moment – 2017 will bring more machines, software and apps that'll further swell the data highways. We will probably never hit a data ceiling so I can't overstate the importance of a sound and forward-looking data management strategy now.

Central to that strategy are things like business intelligence: drilling quickly to the truth in your data. Storage: expensive server farms versus the Cloud. And security: Tesco got hacked, TalkTalk got hacked – the threat is very real.

Unfortunately, fix-all, pan-department, off-the-shelf AI systems aren't available. So, automated platforms, AI, robots – call them what you will – need to be mapped, developed, integrated and trained. And this data management strategy can't exist in isolation: banks need to roll it up as part of a wider digital strategy, and as part of an overall business strategy.

For starters, new talent is required to develop, design, deploy, analyse and work with new technologies, while current employees will need to be reskilled for a new reality.

Then there's clients and customers. Institutions that are able to construct and manage efficient, intertwining data flows must find ways to push benefits down the chain.

Like it or not, banking is not a trusted industry. Putting more automation between customers and their money or goals may be a bitter pill to swallow. In addition, the AI push will see certain people nudged out of jobs, so banks must think about payoff.

Customers aren't daft. Facebook, Google, Uber - we wearily trade our data in exchange for what, in the end, are personal, hyper-relevant services. Banks need to, basically, come up with their own 'crystal ball' technology.

Uber knows where you are, before, during and, now, even after your ride. It knows where the driver is; how much you'll pay; what service you require.

Uber has a crystal ball. But all that goes to show is that we're not staring down an impossible task. Banks have power, reach and resource at their disposal so my last point, which might sound laughable after all that, is to try and keep things simple.

A comprehensive data strategy for your bank may include only a dozen key end goals, so start there and work back: there are some great brains out there to help you with the detail.

Banks need to believe in and invest in a future made of data. If you don't, the others will.

In fact, the others are.

If you’re a bank looking at AI solutions, I advise you to consider

Where can you apply AI and how to set it up?

How quickly can you adopt an AI solution?

How to manage your team's transition through this technology upscale;

What do you need to do to your existing infrastructure to make this successful?

Tying business strategy closely with technology strategy;

Taking baby steps, solving one problem at a time;

Building the right partnerships to facilitate the transition.

SimCorp recently announced the results of a comprehensive survey, titled 'Realizing Growth Through Operational Agility', which examines the current state of IT and operations in the global buy-side investment management industry and includes several notable findings. This includes the fact that 47% of the surveyed firms lack confidence in either their IT infrastructure, their data, or both.

The results also show that firms that are confident in both their data and infrastructure are much more likely to pursue a growth strategy than those with data/infrastructure problems. Further, firms with a lower degree of confidence in data or infrastructure are more likely to increase IT spend in the future, according to the survey.

The availability of real-time data in the front office is generally perceived as an important factor in buy-side firms' ability to make quality investment decisions. The survey shows that almost half (47%) of the respondents do not have access to real-time data in the front office. When breaking this down by IT strategy, the findings show that more firms running on 'an integrated investment management solution' have access to real-time front office data than those running with 'a core platform with multiple add-ons' or a 'best-of-breed strategy'.

Other findings include:

• The integrated strategy - either a solution from a single supplier or a core system with add-ons - is preferred by the majority (53%).

• The average rate of manual processes in the front office (measured as time spent per day) is 19%, or 1½ hours out of an eight-hour workday for each employee.

• On average, firms operating with a best-of-breed IT infrastructure have higher manual process rates than those running with an integrated solution (24% versus 17%).

David Beveridge, Senior Product Marketing Manager at SimCorp commented: "Having roughly half of all surveyed firms express a mistrust in either their IT infrastructure or data is alarming. While this is damaging to the firms' own ability to generate growth, the ultimate losers could very well be their clients. The survey results clearly suggest the integrated solution strategy as the most viable path to higher operational agility and efficiency."

The survey was conducted in mid-2016 by the market research firm Lindberg International and covered 150+ respondents worldwide. For a full presentation of survey results and conclusions, please download the white paper: 'Realizing Growth Through Operational Agility'.

(Source: SimCorp)

According to a survey of nearly 1,000* senior finance professionals, non-financial data may be the game-changer for forecasting success. This finding was revealed as CFOs admitted that non-financial data capture ranked only fifth in their top five priorities, despite the proven benefits when planning, budgeting and forecasting.

The Future of Planning, Budgeting and Forecasting Survey, carried out by the FSN with members of its Modern Finance Forum was commissioned by Advanced, the UK’s third largest software and services provider, to understand how financial decision makers can get ahead with better data-driven decision making.

The findings revealed that CFOs who make better use of non-financial data are:

• more than 1.7 times more likely to forecast earnings within plus or minus 5%
• two times more likely to be able to forecast beyond the 12 month time horizon
• two and a half times more likely to be able to respond to market change

“The survey shows the latent potential of non-financial data to transform the accuracy of business forecasts. It’s no exaggeration to say that it is a game-changer yet CFOs rank it a lowly fifth in their priorities for the forecasting process,” says Gary Simon, FSN’s chief executive officer and the leader of the Modern Finance Forum on LinkedIn.

“The current business climate is characterised by huge business uncertainty yet the effective use of non-financial data allows businesses to extend their planning horizon, improve forecasting accuracy and improve decision-making.”

“It’s clear that many CFOs are missing a trick when it comes to recognising the value that a connected business can offer. Connected CFOs will ensure every board member - but especially the CEO - has an integrated and real-time view of the projected financial performance of the business. However it is vital that this financial insight is inextricably linked to the operational performance of the business, informed by areas such as people skills and the development and impact of digital transformations for example. This is the silver bullet to give every organisation the best chance to drive efficiencies, productivity and growth across every aspect of the organisation, comments Andrew Hicks, CFO at Advanced.

A full infographic reveals further results from the research, such as the top four priorities for CFOs being:

• Greater simulation and scenario planning
• Engagement with more non-financial stakeholders in the PBF process
• Shift to rolling forecasts
• More sophisticated analytical techniques

*There were 955 people of the Modern Finance Forum who responded to the survey were senior finance professionals covering 23 countries and 13 industry sectors.  Approximately half of the respondents were from organisations with more than 1,000 employees.

(Source: Advanced)