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[ I ] f you’re a chief risk officer (CRO), your world is getting more

complicated and, at the same time, simpler. It’s more complicated

because as customer experiences grow faster and more frictionless, so

does risk. It’s simpler because new technologies, powered by attainable

artificial intelligence (AI) and machine learning platforms, are helping you

manage this complexity.

In a word, this complexity is about data. Humans are creating more data every

day than we did in the first 100,000 years of our existence, and the rate of data

proliferation continues to rise. Imagine that a gigabyte is the size of a brick. Ten

years ago, we created enough data to build a house every day. Now we produce

enough data – day in, day out – to build two and a half Great Walls of China.

Data is not only growing in volume, but also in format and channel.

Beyond the data explosion, CROs also face the growing demands of today’s

customers. They are always ‘on’, and they expect frictionless experiences –

fast. CROs are tasked with creating processes for risk management that can

keep pace with these demands for speed and ease of use.

The result is that organisations are changing, beginning at the top. Where

there used to be a division between the CRO and the chief information officer

(CIO), now there is a tight coupling. Data used to be the sole domain of the

CIO. Not anymore.

CIOs are tasked with managing and governing data, but that’s not

possible without understanding the meaning and the insights behind the

data. And CROs, tasked with understanding the data, can’t do their job

unless they understand how the data is managed and kept safe.

Gone are the days when silos separated these two concerns: the

infrastructure piping the data through an organisation, and the information

flowing through these pipes. Today, the CRO needs to help manage the

infrastructure, and the CIO needs to understand the content and the context

of the information that’s inside. In this partnership, the CRO has become >>

With today’s financial organisations built on digital, the lines between managing information and risk are blurring

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>> the CIO. It then begs the question: has the CIO, in

turn, become the CRO?

The data breach epidemicThis partnership between the CRO and the CIO has come

just in time, because the complexity underlying our

global risk infrastructure is untenable. The result is an

epidemic of data breaches worldwide. These breaches

are both frequent and massive, and they demonstrate

an infrastructure that’s broken.

In one study, the Association of Certified Fraud

Examiners found that the global cost of fraud amounts

to £2.8 trillion every year. If fraud had a GDP, it would

approach the size of Germany’s.

To navigate the complexity of detecting fraud across

such enormous volumes of varied data, financial

institutions are increasingly deploying machine

learning systems that can pinpoint patterns with an

accuracy that was impossible before. The predictive

power of AI is so great that deploying machine learning

platforms is becoming an essential requirement for

organisations large and small.

The result is another partnership. Just as CROs and

CIOs are coming together, so are humans and machines,

combining their intelligence and their learning

processes to create a sum that’s greater than its parts.

The human and machine partnership is not without its

difficulties, however.

“The biggest challenge for risk is understanding and

adjudicating what I need to pay attention to, whether I

am an internal auditor or a chief risk officer,” said Craig

Muraskin, managing director for innovation at Deloitte,

speaking to Forbes in 2016.

“There are limitations to what humans can do, what

they can find. Why we are so keen on the technologies

is that we think there is great opportunity to unearth

levels of insight not previously possible when we are

dealing with enormous volumes of data.”

To meet the immediacy that customers demand,

financial services providers have mere milliseconds

to make decisions that affect their clients. One major

concern is reducing false positives, where legitimate

customers are blocked due to a mistaken red flag.

Effective decision-making should be able to reduce

false positives as well as criminal behaviour. In other

words, risk management is about more than fighting the

bad guys. It’s about growing a business.

Impact of digitalisationWhen the ancient Chinese started using paper money

in the tenth century, they used paper from mulberry

trees. To prevent fraud, guards watched over the

mulberry forests.

A thousand years later, commerce has become

more complex. And so has our money. Currency has

become intangible. Armed guards no longer make for a

good security strategy, because now money exists as

ones and zeros transmitted across mobile phone masts

and satellites. We’ve migrated our economy to a digital

infrastructure. And that infrastructure is vulnerable.

Criminals are scrambling to find back doors into the

world’s biggest institutions. And they’re succeeding,

often in surprisingly simple ways. Consider the breach

two years ago at the large American retailer Target,

where 110 million people had their data stolen. The

breach cost the retailer $116 million in settlement

money. It also cost the CEO his job.

How did the fraudsters get in? Through the

air conditioning. Fazio Mechanical is the heating,

ventilation and air conditioning company that helps

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“ Today’s fraudsters are so creative that one survey of British bankers put ‘evolving criminal methodologies’ as the largest financial risk to business”

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[A]s data analysis becomes more and

more central to financial services,

the available talent pool of data scientists is

shrinking. At the same time, there is a boom in

the innovation of new tools and software that

will change the ways that data scientists work.

In the old world, data scientists, even the

most senior and creative ones, had to spend

much of their time completing tedious but

critical tasks, handcrafting strategies for data

hygiene, feature extraction and model updates.

But new tools and software have arrived that

can automate these mundane tasks and save

the data scientists precious time.

At the other end of the spectrum from tedious

tasks, there are advanced techniques. In the past,

only top analysts

could perform a cer-

tain tier of advanced

analytics work, using

sheer brainpower

and creativity to

wrest meaning out

of raw data. But for

the data scientist of

tomorrow, advanced

analytics will become

accessible for a

much larger popu-

lation, thanks to the

attainable insights

that machine learning is beginning to provide.

With the help of a human to train and provide

feedback to the machine, systems using AI are

modelling the universe with fresh eyes. They

are lending these new perspectives and insights

to the data scientist in a partnership that builds

on itself and improves over time.

The drawback is that sometimes it’s

impossible to find out why a machine learning

system has reached a certain conclusion. The

shrouded thinking of these systems creates

a conundrum for CROs, who are bound by

regulation to document the whys behind their

decisions. The disadvantages of AI that happens

inside a black box call for a new kind of machine

learning: ‘whitebox’ AI. This more transparent

type of AI provides explanations of the top-most

factors leading to its decision, using language

that a human can understand.

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Target keep its food and drinks cold. One day, an employee

of this small vendor acted on a phishing email. Hackers

got their login. From there, it was just a few more steps up

into Target’s servers, and down into Target’s point of sale

systems, where the valuable data was stored.

Stolen data such as this feeds a booming black market –

an economy unto itself where fraudsters sell an individual’s

stolen information for the cost of a sandwich.

New skills for a new worldThe wildly multiplying vulnerabilities in our digital

infrastructure, and their associated risks, mean that

CROs and CIOs alike will need to expand their respective

skill sets to survive this new world order. Risk is digital,

so risk solutions must be digital as well. Criminals

are keeping close tabs on organisations and their risk

systems, and are scrambling to find those organisations

with the weakest links. And as the flurry of digital

breaches demonstrates, rules-based systems aren’t

cutting it anymore.

Machine learning to the rescueTraditional approaches to making decisions rely on

identifying risk patterns using human-created rules. The

problem with this approach is that financial institutions

are constantly addressing yesterday’s threat, leaving

vulnerable entry points open for the next wave of attacks.

While criminals can adapt their tactics every day, it can

take three to six months for conventional fraud detection

products to catch up. By contrast, anti-fraud systems based

on machine learning can recognise suspicious patterns in

an instant, even when the context changes.

Traditional approaches to fraud detection face a further

threat: despite the high financial and reputational impact

of fraud, its numerical incidence is relatively low. Fraud’s

prevalence is also spiky across time, with sporadic and

seemingly unpredictable variations.

Further, each individual case of fraud may not share

a consistent set of characteristics, and with traditional

approaches, the result is either failed identification or over-

flagging, a costly burden on any organisation.

Compare this approach to machine learning. Platforms

that leverage the power of AI can ingest enormous

quantities of data in every format, and from many

channels. This allows them to benchmark the normal

behaviour profiles for individual entities such as a person,

device or ATM. These platforms then track abnormal

behaviour in real time, replaying four years’ worth of

transaction history in an instant to alert decision-makers

to suspicious activity as it’s happening.

Largest single financial risk to business at present time

Greatest area of investment in financial crime prevention 2015–17

Cybercrime

Fraud

AML

37%

20%

23%

Sources: LexisNexis® Risk Solutions report produced for the British Bankers’ Association, November 2015; Financial Fraud Action UK report, Fraud: The Facts, 2016

42% evolving criminal methodologies

15% cost of AML compliance 11% lack of personnel in your risk function

10% civil prosecutions/class actions

10% geopolitical events

10% sectoral sanctions

2% other

2015 card fraud losses by type

ID th

eft

Card

not

rece

ived

Coun

terfe

it ca

rd

Lost

/sto

len

card

13%8%7%2%

Remotepurchase 70%

2015 financial fraud losses by type

Cheque Remote banking

Payment card

75%22%

£755M

£1.76BN

3%

Totallosses

Prevented fraud

+26%

70%

2014 2015

of attempted fraud

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[A]s a CRO, this question stands out: what is

cyber risk? At the heart of this question is the

most discussed topic in corporate boardrooms today.

Hack attacks, impersonations of real people, cyber

breaches... the list of threats keeps growing.

How do we manage this risk? My advice: be practical.

Take a layered approach, and make sure that all the

controls you think about will account for ongoing risks.

CROs need to do their best at confirming a customer

is truly the customer. Then look at the next layer. How

is the customer transacting? Are they exhibiting the

appropriate behaviour?

Some layers are about the use of stolen data, while

other layers are put in place to prevent data loss. Enter

the CIO. That role is tasked with making sure the firewall

is up to date, and scanning systems to avoid entry by

unauthorised vendors. The day-to-day engagement

between these two forces happens in an overlap that

can be described with one word: risk.

Organisations that are more sophisticated will

hire penetration companies, with ‘white hat’ hackers

who help orchestrate attacks on systems searching

for vulnerabilities. Other organisations will act out

scenarios and fictitious cases to test their fire drills.

There’s an opportunity here to learn more about the

behaviour of your portfolio, and also to meet the growing

expectation from regulators for the financial industry

to know its customers, get better at predicting risk,

and improve customer service. What you see is a lot of

companies throwing people at the problem.

But there’s another thing to throw at the problem. Is

machine learning on your radar? Numerous financial

institutions have risk issues, and the prevailing answer

to date has been to put more bodies on it. The bodies do

quality assurance, checking the checkers and checking

the checking. This is mildly effective, but it’s also

extremely costly.

It is time to apply AI to these problems. The

efficiency of machine learning is light years ahead of

piling on more people. Its efficacy and its ability to stop

fraud, make better credit decisions, and improve the

customer experience is far more advanced than any

human can do. The regulators will like it too.

CROs have long waited for change that puts them

on the cusp of seeing AI in action. To incite change and

bring AI to your organisation, ask yourself what’s holding

things back. Is it inertia – the impulse to continue what

“ What you see is a lot of companies throwing people at the problem. But there’s another thing to throw at the problem. Is machine learning on your radar?”

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[ I ] n the past, AI was only available to big governments or

the world’s largest corporations. Now AI is becoming

such a vital tool, and so distributed, that it is joining a list of

essential utilities, much like electricity and gas. With AI like

running water, all businesses are eager to drink from the tap.

Here are five reasons why AI and machine learning are such

powerful decision-making tools:

Pattern detection – Criminals are people with money

to earn and things to do. They are working at a feverish pace to

keep up with the latest technologies. So criminals change their

methodologies until they find one that works. Build a wall and

they’ll dig a tunnel. Machine learning explodes this rules-based

paradigm, using insights gleaned from a mountain of data points

to surface patterns that were previously invisible.

Omnichannel and omnidata ingestion – A good

machine learning platform is not a picky eater. It will take in all

the information you feed it, no matter the format or the channel.

For example, an anti-fraud policy needs to construct a complete

view of the transaction by taking in data from disparate channels,

including online, mobile, contact centre and in-store routes.

Long-tail navigation – Risk is long-tailed. Fraudsters work

hard to avoid being caught, so there may be no single consistent

giveaway to their crime. Identifying potential criminals requires a

complex scoring and weighting of a large number of behaviours.

It’s a task whose scope can only be handled by machine

learning, ideally using a platform that offers transparency into its

workings, with whitebox explainable AI.

Speed – The motto of any protection system: don’t inhibit

sales! Customers grow sour by the millisecond. They want a

frictionless, fast experience. Machine learning is a match to this

high demand, offering real-time protection that’s so fast and

effective it goes unnoticed.

Self-improvement – The hallmark of machine learning is

that it gets better over time. In a nod to the partnership between

the human and the machine, data scientists and machine

learning models build on each other’s insights in a positive

feedback loop. A good machine learning platform will automate

parts of this feedback loop by automatically self-improving based

on experiential data.

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you are doing and not stopping to change what seems

to be working? Or is it bureaucracy – the difficulty of

manoeuvring people and systems to effect change?

Keep in mind that regulators don’t want untested

technology thrown at customers. So the best way

to bring about change is to find an innovative streak

within your organisation that will help you deploy and

test to make a solid business case. Align with potential

financial technology disruptor teams that offer a better

experience for the customer. The rest will follow.

Nobody likes fraud. My advice for my peers is to

try and be open minded and test some of the new

technologies. Can AI and machine learning be the bridge

between the CIO and the CRO? I hope so.

Machine learning is already beginning to “replace manual

data wrangling and data governance dirty work”, leading

to embedded data analytics software providing US

companies with savings of over $60bn by 2020.

AI in general is expected to add up to an additional 4.6

per cent to the US gross value added (GVA) by 2035,

representing an additional $8.3 trillion in economic activity.

(Source: Forrester quoted by Forbes)

$60bnsaving by

2020

+4.6%GVA by2035

$8.3tn

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[T]he risk in our financial systems is systemic. So it’s

a good thing that we are here to witness the dawn

of attainable AI. It’s a revolution that almost didn’t happen.

The term “machine learning” was coined by an

American named Arthur Samuel. In 1962, he taught a

computer to play draughts. It worked by using a scoring

function that measured the chances of winning for each

side at any given position, taking into account a handful

of signals.

What made this the first of its kind was that

the program then used “rote learning”, meaning it

remembered the past and re-evaluated its decision-

making based on what it had learned so far. It took the

program over a decade to gain enough skill to challenge

a good human player. Samuel had a belief that teaching

computers to play games was an entry point for solving

bigger problems. But across the Atlantic there was a

man who disagreed.

Sir James Lighthill was a British mathematician who

specialised in fluid dynamics. While Samuel’s computer

program was perfecting its draughts play, Lighthill was

writing a report for Parliament declaring AI a failure. He

said AI algorithms wouldn’t work on real problems, just

toy problems.

The report led to a complete dismantling of AI

research in England. The pattern was repeated across

the globe. Public funding for AI was cut from research

programmes from America to Japan.

This started an AI winter that lasted until the 1990s.

But now there’s a resurgence of passion and funding

for AI that is unmistakable, and undeniable. Why is this

happening only now? There are five distinct threads

of technology that have come together in an amazing

moment of convergence...

Affordable parallel computing – About a

decade ago, Google innovated a method for computers

to work in parallel that introduced us to a new order of

magnitude for processing power.

Faster processors – Before, we just had one kind

of processor: the central processing unit, or CPU. Now

we have a second: the graphics processing unit, or GPU.

It’s a hardware renaissance, and it’s opened up a new

dimension of computing for machine learning.

Cheaper, smarter data storage – You are

aware of Moore’s law. It describes the exponential

growth for our capacity to store memory. Experts keep

predicting that Moore’s law has got to slow down at

some point. In fact, there’s no end in sight.

Big data – In reality, the name would more

accurately be “really, really, really big data”.

Better algorithms – Maybe you remember voice

recognition software 15 years ago. It took three months

of training before it could recognise your voice. Now

this software can recognise any voice, instantly. The

reason? We invented a better algorithm. Every time we

do that, we have the power to model the universe with

even more accuracy.

Add this all up and you have a revolution. A quiet

revolution. The end result is a complete restructuring

of the world’s digital infrastructure. To read more

about the exciting implications of this convergence,

consult our booklet The Dawn of Machine Learning for

Banking and Payments.

What’s the roadmap for good AI plumbing? How do

we operationalise AI so that it helps us and doesn’t hurt

us? The next few years will be exciting times as we

figure out the answers to these questions, with CROs

addressing the practical matter of how to effectively

pipe AI through an organisation.

As we approach a state of good operations in AI, it

may turn out that machine learning is about human

learning after all.

For decades, AI has been waiting in the wings. Only now, conditions are right for it to grab the spotlight

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The AI Convergence

info@feedzai.com