machine 2017 learning for financial services · 2020-04-16 · an intelligent environments white...

An Intelligent Environments White Paper

Digital Financial Solutions

Machine Learning for Financial Services

2017



Machine Learning for Financial Services P 3

Conventional software succeeds because it’s good at many of the things we’re not so good at: executing instructions over and over, with unwavering energy and discipline, at tremendous speed.

For 70 years, conventional software has been the brawn to our brains. A faithful companion that performs remarkable feats of computation without challenging our comforting position as the source for the code; the designer of the rules; the seat of intelligence.

Well, human, say goodbye to your comfort blanket.

Machine Learning doesn’t need our rules. It succeeds by being better than we are at the things we’re good at: the learned, insightful, creative, what-if, fuzzy-shades-of-grey type thinking. Machine Learning software learns quickly creates insight, is nuanced, and never forgets.

Practical and affordable Machine Learning solutions are now overturning our expectations of computing and knowledge work. Once the domain of math geniuses and computer geeks, this technology is now popping up in all kinds of software. Machine learning engines are available via the cloud, with literally thousands of engineers at Amazon busily developing applications, to name just one company in the deep learning space. The technology is fast becoming mission critical for Financial Services by addressing a whole slew of previously unsolvable problems.

Yet, whilst almost nobody doubts that Machine Learning is a superior hammer, it’s not yet clear where the nails are for Financial Services, or where to find the problems for this solution.

This is my advice on where to aim your hammer.

For more information please contact [email protected]


P 4Machine Learning for Financial Services

What is Machine Learning

“

“ ...gives computers the ability to learn without being explicitly programmed.



Table 1, Attributes of conventional software and Machine Learning

Conventional Software Machine Learning

Deterministic Non-deterministic

Analytic Heuristic

Programmed Trained

Adapts manually Adapts automatically

Operates within rigid parameters Extrapolates and generalises

Starts with tight assumptions Starts with no assumptions

Machine Learning is a branch of AI (Artificial Intelligence), and a keystone technology for much of today’s most disruptive technology including self-driving cars, natural language processing and genomics.

The term, coined by Arthur Lee Samuel in 1959, refers to a field of study that “gives computers the ability to learn without being explicitly programmed.”i Instead of being given a set of instructions and static parameters to follow, the software learns how to operate through its own data analysis. Once taught, AI systems can generalise and derive insights that conventional computer programs can’t.

Machine Learning attributesMachine Learning’s great leap forward is that it is more organic and less rigid than conventional software. This apparent lack of structure makes it more difficult to build, but more flexible in its application. The table below compares some aspects of conventional software and Machine Learning to help you get a feel for why Machine Learning is uniquely placed to solve particular problems.



“Siri, why is Machine Learning suddenly so popular?”Machine Learning is exploding in popularity. In 2016 it reached the summit of Gartner’s Hype Cycle for Emerging Technologiesiii and, according to IDC, we spent $8 billion on cognitive and AI systems that yeariv. This year we’ll spend $12.5 billion and by 2020 that will rise to $46 billion.

“This is deep learning’s Cambrian explosion,” - Frank Chen, Partner at Andreessen Horowitz

That investment is happening because Machine Learning is a game-changing technology.

According to Netflix, its Machine Learning-based personalisation and recommendation system saves “more than $1B per year.”v Some commentators have gone so far as to suggest that AI will be the principal engine for global growth over the next 20 years.

Like all good “overnight success” stories, Machine Learning has been around for a long time. What may have changed everything was big data and the lower cost of computing power. Companies like Google, Apple, Amazon and Facebook need Machine Learning to make sense of all their data, and they have the vast amounts of cheap computing power to make Machine Learning practical.

Financial Services firms are using it too, and only the unwary will ignore how Machine Learning can be leveraged to create new competitive advantage in the industry.

Machine Learning in your inboxAnti-spam filters use a Machine Learning approach (Bayesian filtering) to let through the emails you want and weed out the ones you don’t. It’s an instructive example of what a successful application of Machine Learning looks like.

• It learns. Before it can stop junk mail, the spam filter must be trained using a few hundred examples of good and bad emails.

• It generalises. Once trained, the spam filter can guess with a high degree of success if an email it has never seen before is good or bad.

• It adapts. The filter updates its training when you mark emails as junk. By doing this it adapts to spammers’ changing tactics and learns your individual preferences.

• It is never 100% right. Without Bayesian filtering your inbox would be unusable (About 60% of all email traffic is spamii) but it cannot eliminate spam completely.



Machine Learning in Financial Services

“

“ Machine Learning is important because it can solve problems in a way that no amount of human brain power or conventional software can solve.



Machine Learning is important because it can solve problems in a way that no amount of human brain power or conventional software can solve.

When compared to human intelligence, AI has several advantages. It can:

• Reduce operational costs,

• Operate 24 hours a day,

• Improve accuracy of decisions, and

• Deliver a better experience to the customer.

Because of this, AI is set to revolutionise knowledge work in the same way that robotics has revolutionised industrial manual labour. In the next 20 years, AI is poised to handle knowledge tasks currently handled by humansvi.

“Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing ... we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies. Furthermore, the value proposition of cognitive systems aligns well with industry executives’ chief priorities.”vii

- Jessica Goepfert, Program Director, Customer Insights and Analysis at IDC

According to a survey of 100 Financial Services executives by the National Business Research Institute and Narrative Science, “32 percent of the group confirmed they were using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.”

McKinsey reports that some European banks using Machine Learning have experienced “10 percent increases in sales of new products, 20 percent savings in capital expenditures, 20 percent increases in cash collections, and 20 percent declines in churn.” viii



Clearly the use of Machine Learning in Financial Services is growing. Successful application of this specialist technology, however, requires an appreciation of the specific class of problems it is best suited for. Applications suitable for Machine Learning typically have the following characteristics:

1. You want a prediction about something, not a definite answer.

2. You can provide a comprehensive set of example data about the problem.

3. You have a continuous stream of similar data to your sample set.

4. You are not trying to predict something that will be materially impacted by external data not included in your data stream.

Table 2, Examples of good and bad Machine Learning problems

Good Machine Learning Problems Bad Machine Learning Problems

Predict the likelihood that a customer will default on loan.

Predict profits from the introduction of a completely new and revolutionary product.

Use face recognition to determine if a person is who they say they are.

Predict next year’s sales from past data, when an important new competitor just entered the market.

Existing, in-the-wild Machine Learning applications relevant to the Financial Services sector already include:

Cybersecurity

Product Recommenda-

tions

Fraud Detection

Natural Language Processing

Credit Scoring

Biometric Authentication

Sentiment Analysis

Chat Bots

Market Personalisation

Money Laundering Detection

Optical Character

Recognition

Compliance

Face Recognition



Rising of the bots The business case for chatbots is clear: it can take a human an hour to respond to a customer query,ix but a chatbot can respond to an almost limitless number of people, within seconds, 24 hours a day for as long as the customer would like to chat.

Gartner predicts that by 2020 autonomous software agents will participate in 5% of all economic transactionsx. Chatbots have the potential to automate almost 50% of Services tasks currently performed by humansxi. Juniper Research forecasts that chatbots will be responsible for cost savings of over $8 billion per annum by 2022xii.

Chatbots are only practical thanks to Machine Learning. Human languages don’t follow a set of formal rules, so programming conventional computer algorithms to understand human language is all but impossible. Because Machine Learning is taught rather than programmed, it can learn to understand what we mean, even if the words don’t exactly add up to that meaning. It can adapt to different language patterns, grammar and dialects – even as they change over time.

Virtual agents like Siri, Alexa and Cortana are marching up the adoption curve and they’ve arrived in the Financial Services sector too. RBS, NatWest and the Swedish SEB group all deploy bots based on IBM’s Watson technology, AI that handles call centre traffic by responding to customer queries.



Other examples of AI-powered Financial Services bots include:

WeChat

Bank of China, China Construction Bank and China Merchants Bank have all deployed WeChat, a messaging and call platform with 650 million active users.

Alexa

In March, Amazon and Capital One announced that customers can now pay their bills by talking to a bot running on Alexa, Amazon’s intelligent personal assistant on its Echo device.

MasterCard

MasterCard Labs partnered with Kasisto, makers of “conversational AI platform” KAI, to create a bot for banks that will launch next year, providing a way for consumers to shop and transact in Facebook Messenger, and pay using MasterPass.

PayPal

In the US, Facebook is rolling out a native payment solution that will allow third party merchants to accept PayPal payments in their Facebook Messenger bots. Customers will be able to make payments in Messenger, link their PayPal accounts, to their Facebook accounts and receive receipts via Messenger.

As an early pilot of this capability, PayPal’s Braintree partnered with Facebook and Uber in December 2015 to allow users to hail and pay for an Uber ride from Messenger.

Lakshmi

Bots aren’t just virtual: India’s City Union Bank is trialling Lakshmi, a robot for handling customer enquiries inside branches. If the robot proves popular the company plans to install it in as many as 30 branches. xiii



Intelligent cybersecurityIn some fields Machine Learning is set to displace human labour. In cybersecurity it will help to make up for a growing shortfall of skilled labour.

“There are not enough cyber specialists in organizations to deal with the number of threats today, and the imbalance will likely become much worse”xiv

Machine Learning can analyse patterns at a speed that humans can’t match and detect anomalies that conventional, rules-based software will miss. Compared to humans using deterministic software for cybersecurity, Machine Learning can:

• Sift reams of log scans and detector data in real time.

• Provide early-warning of potential threats.

• Detect security threats and attacks in real-time.

• Identify zero-day attacks.

• Immediately act to self-protect a system from data loss.

• Use behavioural biometrics to identify users.

• Find authenticated hackers.



Case study: AppSensorFSAppSensorFS is Intelligent Environments’ ground-breaking security “nervous system” that adjusts the Interact® application’s security posture in response to events occurring within the digital financial software platform. It deploys detectors within the application to monitor user behaviour and other events, sending an alert or automatically taking action if it identifies a potential security threat.

The first version of AppSensorFS used a deterministic approach to detect potentially hostile user behaviour across sixty detection points. If the type, sequence and frequency of user interactions matched a known pattern, then AppSensorFS would log an alert and change the security posture of the platform.

Machine Learning is now supplementing this deterministic approach with a non-deterministic capability that can catch atypical or previously unseen hostile behaviour.

A joint team of researchers from Intelligent Environments and Queens University Belfast monitored how real users used Interact and tested the ability of different Machine Learning techniques (both supervised and unsupervised) to create an accurate representation of normal user behaviour.

Armed with the most successful algorithm from the testing, AppSensorFS has the capability to identify

When Intelligent Environments wanted to improve the capabilities of its AppSensorFS software using Machine Learning it turned to the Queen’s University Belfast for world-class assistance. The university’s Centre for Secure Information Technologies (CIST) conducts research into cybersecurity with multinational industry partners like IBM, Cisco, Infosys, Intel, McAfee, Altera and Thales.

anything that lies outside of that classification of normal user behaviour, assign a level of risk to it, and take action accordingly.

Machine Learning:

• Detects hostile behaviour that deterministic models don’t or can’t.

• Makes the classification of behaviour as malicious or benign more efficient.

• Can generate insights that can be formalised as deterministic rules.

• Adapts in response to changes in threats.



CollectionsAt first glance, the process of collecting money from people who are in arrears might look like a straightforward, conventional process: get a list of debtors and phone numbers, and start calling them at awkward hours. This approach doesn’t work very well in our digital self-service world. It tends to be one approach for all debtors, delivering a poor customer experience.

Research shows that Machine Learning models are better able to segment delinquent borrowers, and can identify where proactive advice can help keep customers out of debt. A Machine Learning model developed by the Massachusetts Institute of Technology has shown itself to be “surprisingly accurate in forecasting [of] credit events 3 to 12 months in advance”. The model outperforms conventional techniques, and delivers cost savings of between 6% and 23% of total losses.xv The researchers also found that Machine Learning approaches were more adaptive and “are able to pick up the dynamics of changing credit cycles as well as the absolute levels of default rates.”

“Companies using Machine Learning have been able to reduce their bad debt provision by 35 to 40 percent.” xvi - McKinsey

Machine Learning seems destined to shake up collection strategies too. It is used to select the most appropriate collections strategy for a given customer, enabling a tailored approach to collections that delivers higher performance than the traditional one-size-fits-all approach.



Collections is an emotional business. The stress of falling into arrears tends to accentuate the importance of what, when, and how you speak to customers. Taking the right approach can have a significant effect on the outcome. It is an environment ripe for a test-driven, personalised approach that maximises collection performance and customer experience outcomes.

In Machine Learning, the system selects the best strategy based upon continuous correlation of parameter inputs to outcomes. It predicts the right approach for communicating (email, SMS or call), time-of-day, and many other collections approach variables based upon the outcomes produced by such methods with similar customers.

“...most of the [Accounts Receivable] collection actions nowadays are still manual, generic and expensive ... it seldom takes into account customer specifics, neither has any prioritizing strategies.” xvii

- Predicting and Improving Invoice-to-Cash Collection Through Machine Learning

Table 3, Comparison of strategy selection methodologies

A/B Testing Multivariate testing Machine Learning

Easy to set up Complex to set up Complex to set up

Only tests two variations Tests multiple variations Tests multiple variations

Cannot adapt Cannot adapt Adapts continuously

Takes a long time Requires a large sample size Can run perpetually

Takes a long time

P 16


Machine Learning for Financial Services

Conclusion Machine Learning is a sophisticated, general-purpose technology that’s having a real-world impact today. Although long awaited, AI is now becoming practical within a whole strata of knowledge work that sits beyond the reach of conventional software. Our knowledge economy stands poised for automation, just as automation has fundamentally changed our industrial economy.

Financial services firms can leverage Machine Learning to cut costs and deliver superior services to their customers. Those firms that fail to take advantage of this emerging technology will fall behind.

Intelligent Environments is driving forward with a generation of products that can make Machine Learning an integral part of your business. Our recent developments include detecting cybersecurity threats, smart loan origination, virtual assistants, and optimising collections.

Machine Learning is now solving some of Financial Services’ toughest business problems.



References i. Machine Learning and Optimization; Andres Munoz. https://www.cims.nyu.edu/~munoz/files/ml_

optimization.pdf

ii. Global spam volume as percentage of total e-mail traffic from January 2014 to March 2017, by month; Statista.- https://www.statista.com/statistics/420391/spam-email-traffic-share/

iii. Gartner’s 2016 Hype Cycle for Emerging Technologies Identifies Three Key Trends That Organizations Must Track to Gain Competitive Advantage; Gartner. - http://www.gartner.com/newsroom/id/3412017

iv. Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide; IDC. - https://www.idc.com/ getdoc.jsp?containerId=IDC_P33198

v. The Netflix Recommender System: Algorithms, Business Value, and Innovation; Carlos A. Gomez-Uribe, Neil Hunt. - http://delivery.acm.org/10.1145/2850000/2843948/a13-gomez-uribe.pdf

vi. The Future of Employment: How Susceptible are jobs to computerisation; Carl Benedikt Frey, Michael A. Osborne. - http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

vii. Machine Learning And AI Spending To Surge Toward $47 Billion By 2020: IDC; Which-50. - https://which-50. com/machine-learning-ai-spending-surge-toward-47-billion-2020-idc/

viii. An executive’s guide to machine learning; McKinsey. - http://www.mckinsey.com/industries/high-tech/our- insights/an-executives-guide-to-machine-learning

ix. DATA: A massive, hidden shift is driving companies to use A.I. bots inside Facebook Messenger; Business Insider - http://uk.businessinsider.com/statistics-on-companies-that-use-ai-bots-in-private-and-direct- messaging-2016-5

x. Gartner Reveals Top Predictions for IT Organizations and Users for 2016 and Beyond; Gartner - http://www. gartner.com/newsroom/id/3143718

xi. Can Chatbots Help Reduce Customer Service Costs by 30%? - https://chatbotsmagazine.com/how-with-the- help-of-chatbots-customer-service-costs-could-be-reduced-up-to-30-b9266a369945

xii. Chatbot Conversations to deliver $8 billion in Cost savings by 2022. - https://www.juniperresearch.com/ analystxpress/july-2017/chatbot-conversations-to-deliver-8bn-cost-saving

xiii. Lakshmi, India’s first banking robot, unveiled by City Union Bank. - http://www.bankingtech.com/641772/ lakshmi-indias-first-banking-robot-unveiled-by-city-union-bank/

xiv. The future of cybersecurity; Deloitte.- https://dupress.deloitte.com/dup-us-en/topics/analytics/future-of- cybersecurity-in-analytics-automation.html

xv. Consumer Credit Risk Models via Machine-Learning Algorithms; Amir E. Khandani, Adlar J. Kim, and Andrew W. Lo. - http://mitsloan.mit.edu/media/Lo_ConsumerCreditRiskModels.pdf

xvi. Beyond the buzz: Harnessing machine learning in payments. McKinsey. - http://www.mckinsey.com/ industries/financial-services/our-insights/beyond-the-buzz-harnessing-machine-learning-in-payments

xvii. Predicting and Improving Invoice-to-Cash Collection Through Machine Learning; Hu Peiguang. - https:// dspace.mit.edu/bitstream/handle/1721.1/99584/925473704-MIT.pdf



If you’d like to hear more please contact Clayton Locke +44 (0)20 8614 9800 [email protected]

About the Author

Clayton Locke,

Chief Technology Officer, Intelligent Environments

Clayton joined Intelligent Environments in 2012, taking charge of the company’s technology team. He brings over 30 years’ experience in the software development and consulting industry. He has delivered innovative products and solutions to clients in the financial services and telecommunications sectors, including banking, FX trading, enterprise architecture and mobile application development.

Clayton is responsible for technology strategy, development and delivery of the Intelligent Environments product suite. He does this passionately, leveraging a lean software development approach to build high quality software products for the company’s solid base of blue chip clients.

About Intelligent EnvironmentsIntelligent Environments is an international provider of innovative financial services technology. Our mission is to enable our clients to deliver a simple, secure and effortless experience.

We do this through Interact®, our services platform, which enables secure customer acquisition, onboarding, engagement, transactions and servicing across any digital channel and device. Today these are predominantly focused on smartphones, PCs and tablets. However Interact will support other devices, if and when they become mainstream.

We provide a more viable option to internally developed technology, enabling our clients with a fast route to market whilst providing the expertise to manage the complexity of multiple channels, devices and operating systems. Interact is a continuously evolving digital customer engagement platform that ensures our clients keep pace with the fast moving digital landscape.

We are immensely proud of our achievements, in relation to our innovation, our thought leadership, our industrywide recognition, our demonstrable product differentiation, the diversity of our client base, and the calibre of our partners.

For many years we have been the digital heart of a diverse range of financial services providers including Generali Wealth Management, HRG, Ikano Retail Finance, Lloyds Banking Group, MotoNovo Finance, Think Money Group and Toyota Financial Services.

Kingston Upon ThamesRiverview House20 Old Bridge StreetKingston upon ThamesSurrey

KT1 4BU

+44 (0) 20 8614 9800

Belfast, Northern IrelandSuite 1B Ground FloorConcourse Building 1Northern Ireland Science ParkQueens RoadBelfastNorthern Ireland

BT3 9DT

+44 (0) 2890 785 795

Digital Financial Solutions

[email protected]

machine 2017 learning for financial services · 2020-04-16 · an intelligent environments white...

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