WHAT IF EVERY FD HAD THEIR OWN AI?

REVENUE RECOGNITION | EBOOK SERIES | © 2017ARTIFICIAL INTELLIGENCE | EBOOK SERIES | © 2017

How leading finance professionals will transform their businesses with artificial intelligence

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12 pages | 10 minutes

Mention artificial intelligence (AI) to a person on the street, and

you’ll conjure up Hollywood visions ranging from the humanity-

crushing Skynet of the Terminator series to the robot love interests

in Her or Ex Machina. Perspectives on the tangible impact of AI

similarly range from Elon Musk’s declaration of AI as “the greatest

risk we face as a civilisation” 1 to IBM Chief Science Officer

Dr. Guruduth Banavar’s belief that “we’ve never known a technology

that can have a greater benefit to all of society than artificial

intelligence.” 2 The reality, especially in the short term, likely lies

somewhere between these extremes.

1 http://fortune.com/2017/07/15/elon-musk-artificial-intelligence-2/2 https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf

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The quest for AI is not new.

Since Alan Turing’s seminal 1950s work on machine intelligence, AI has passed through several hype cycles. Today we approach the crest of another hype cycle, with more than 2,500 AI and machine learning companies and startups identified.3 The confluence of enormous computing power, globally distributed networks, and vast amounts of data have driven both the interest and potential of AI to unparalleled heights.

While most researchers believe that the pop culture version of strong AI—a sentient, thinking, human-like intelligence—remains decades away, “weak” or “narrow” AI is already here. Narrow AI, defined as non-sentient technology generally focused on a single task, has found its way into recommendation engines developed by companies like Amazon and Netflix, as well as endeavours as varied as insurance underwriting, automated call centre response, and résumé processing.

3 shivonzilis.com

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In this guide,we push past

the mythology to answer the

question:what if every

finance director had

their own AI?

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As with other major industries, financial services has been an early adopter of narrow AI, but the same cannot be said for the core finance department across industries. In a recent global survey, less than 25 per cent of executives in the finance and accounting area felt that AI would enable significant staff reductions in the next five years, significantly lagging behind their peers in supply chain, HR, and even product/service development.4

Part of the barrier may be the perception that AI is “just for big banks.” Projects to automate fraud detection or identify money laundering across global financial networks must take multiple years and deep pockets, so the common wisdom says that an organisation needs the scale and resources of a large financial institution like the Bank of England or the data volumes of Visa in order to reap ROI from an investment in AI. The finance departments of multibillion-dollar organisations, let alone a finance department processing under £1 billion in payables and receivables, may not feel they can justify pursuing AI-powered projects.

As is often the case, the common wisdom is wrong.

Finance departments outside the big banks have several clear-use cases where the use of AI-driven technology makes both business and financial sense—and can deliver tangible ROI. Let’s examine a few.

Is AI just for the big banks?

4 https://www.cognizant.com/whitepapers/the-robot-and-I-how-new-digital-technologies-are-making-smart-people-and-businesses-smarter-codex1193.pdf

65 http://www.afr.com/technology/artificial-intelligence-in-mind-as-commonwealth-bank-develops-future-tech-20161202-gt2nfl

AI for fraud detection

While the big banks get most of the attention today, they are by no means the only ones using AI for fraud detection. Every finance department makes a concerted effort to detect data entry errors and/or internally-driven fraud, whether it’s over- or double-billing of customers, over-billing by suppliers, or more sophisticated embezzlement carried out through the creation of fake suppliers or customers within the finance system. Increasingly, AI technologies can be used to detect each of these different scenarios.

Additionally, in partnership with their IT organisation, some FDs have begun leveraging AI technologies to monitor hackers’ attempts to obtain customer or corporate financial data or to create false suppliers or transfer accounts.5 In the wake of many high-profile data breaches over the past several years, this practice has become top of mind for FDs as well as CIOs. Should such intrusions occur, AI-powered technologies can also be used to minimise the impact of data leaks.

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Human-driven analysis simply can’t operate quickly enough.

But AI can.

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AI for BI

The most forward-thinking finance departments have already begun to use AI to develop value and insight from their finance and customer data.

For most organisations, the sheer volume, variety, and velocity of customer data precludes the type of traditional analysis done with existing analytic or business intelligence (BI) tools.

Even big data or discovery tools too often rely either on sampling techniques driven by the creativity of a handful of data scientists or the establishment of costly data infrastructures with dedicated processing power.

Avoiding those more costly measures can help FDs drive immense value, particularly in the realm of predictive customer behaviour. Consider credit risk and collections. For even small organisations, multiple years of customer data and financial data can quickly add up to several terabytes of data. At this volume, human-driven analysis simply can’t operate quickly enough. But AI can.

Using AI and machine learning to analyse patterns of customer behaviours that have led to late payments or bankrupt customers can yield immediate improvements related to collections and days sales outstanding (DSO).

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Simply imagine the value of not selling an expensive product or service to an organisation that can’t or won’t pay the bill. For a small or medium-sized business dependent on short-term cashflow, choosing the right customers—and avoiding the wrong ones—can make the difference between success and failure.

Avoiding unprofitable customers is just one side of the financial equation: what about making sure you keep your best customers? In the new services economy, unsatisfied customers can quickly take their business—and your profits—elsewhere. Identifying the subtle customer behaviours that precede a profitable customer leaving can be an impossible task, especially when your financial and customer data lie in multiple, disconnected systems. Even if all your data resides in a Hadoop data lake, it could take a data scientist six months to formulate a subset of patterns that lead to customer churn.

With the data volumes generated by modern CRM systems and the velocity of customer choice, AI will be critical for organisations seeking to identify, serve, and retain their most profitable customers.6

6 https://www.salesforce.com/form/industries/financial-services/retail-banking-customer-loyalty-whitepaper.jsp

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How the FD can implement AI today

Organisations have several paths they can follow to bring AI into the finance department. As AI-driven analysis evolves from an interesting project to essential practice, it may be tempting to take the least risky option by building systems in-house. The challenge with “build-it-yourself AI” is that user interface (UI) is the only simple part. Behind that UI, truly robust AI systems require heaps of intellectual property, large teams of data scientists and programmers, a massive amount of training data, labour-extensive data extraction and preparation, and last, but not least, an expensive technology infrastructure.

It’s not simply a matter of recruiting a few programmers from Oxford or Cambridge to “build us some AI”. Even if it only took a couple of smart developers, the chances of finding and hiring them would be slim for most companies. The world’s largest and most innovative businesses—e.g. Google, Amazon, IBM, Baidu, Salesforce, Facebook—are locked in an arms race for the best and brightest in AI. Meanwhile, those same companies are investing in and acquiring emerging AI vendors at a dizzying pace.

117 https://www.forbes.com/sites/aarontilley/2017/07/13/clarifai-ai-image-recognition/8 https://singularityhub.com/2017/06/12/five-fintech-companies-using-ai-to-improve-business/

Some businesses may find value in working with some of the big names mentioned above, but it could be more worthwhile to find a vendor already using AI to solve problems specific to the practice of finance. Even independent AI vendors like Clarifai consistently beat the macro vendors around specific AI challenges like machine learning-driven image recognition for healthcare, video analysis, and ecommerce.7 As another example, collectAI combines your customer and financial data with their AI technology to optimise collections.8

The ideal solution for most FDs, however, will be to simply pick a financial management solution with built-in AI. When on-premise systems were common, it would have been inconceivable to have AI built directly into the system. But expectations have changed now that cloud is standard.

With cloud-based applications, the finance department should expect to see AI capabilities becoming more and more sophisticated in the applications they use on a daily basis.

If your current finance vendor doesn’t provide a roadmap for incorporating AI into their system, it’s probably time to look for a new vendor.

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Sooner or later, built-in, robust AI will become table stakes for the leading application cloud vendors.

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Sooner or later, built-in, robust AI will become table stakes for the leading application cloud vendors. The challenge for customers will not be picking vendors or adopting the best AI technologies; the challenge will be reimagining how they do business today and in the future, how AI will change existing operating models, and how to best take advantage of the transformative capabilities of AI.

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