SDS Data-Lab: Finance edition

SDS Data-Lab: Finance edition

Digging into the data disrupting your world

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Setting the scene

No other concept gives humanity both hope and fear quite like Artificial Intelligence.

Google has invested over $30 billion in A.I. ⁽¹⁾

70% of the US population say A.I. makes them anxious ⁽¹⁾

$100bn

By 2025, the A.I. market value will surpass $100 billion ⁽¹⁾

Elon Musk says A.I. is a bigger threat than nuclear weapons
A.I. is challenging and changing human intelligence. Making sense of what this means for the industry requires considering which part of a business’ core function is delivered by people.

For the financial industry, what people deliver are predictions.
Commodities, consumer behaviour, trends, government regulations and impacts of trade - the list of things the industry must keep in focus is enormous. Knowing this gives opportunities to make better, more profitable predictions. However, A.I. is already disrupting this processes in new and sometimes unexpected ways.

⁽¹⁾ https://www.forbes.com/sites/louiscolumbus/2017/07/09/mckinseys-state-of-machine-learning-and-ai-2017/#646b208375b6

Big Data + A.I. = Data driven business

Big Data is the foundation for A.I. disruption of the finance business model.

What is Big Data?
Big Data can be thought of as the most detailed map of consumers available to a business. It’s an up-to-date, step-by-step, realtime record of consumer activity. But it isn’t a simplification tool. It’s more like trying to measure the volume of Lake Eyre - a dataset defined by consistent change in things like rainfall, rate of evaporation, rate of flow and current climate.

Decoding Big Data is where A.I works its magic.
Without the aid of A.I., no human can harness the potential of these massive data sets. That’s why A.I. is often compared to a sophisticated, adaptable fishing rod - a tool designed for a single purpose, able to develop better bait, a longer line and stronger hooks, and capable of getting better at catching only the most-prized fish.

What does this mean for finance?
Those in the finance industry need to determine how to best combine these two forces and enhance the purpose of their business model. Knowing your consumers results in stronger, more popular services. And as more consumers join your industry pool, datasets grow. This means more insight to innovate and disrupt with.

Disruptive Intersection: Powering consumer culture

Disruptive Intersections: Powering consumer culture

A.I. is powering an on-demand culture that drives consumers to companies like Afterpay.

46% of millennials will pay more for same day delivery ⁽²⁾

In 2018, Afterpay reported a revenue increase of 518% ⁽³⁾

A data-powered culture gives modern consumers instant access to transport, food and shelter; and around-the-clock access to one-day shipping of online purchases. Using payment methods such as Afterpay means they’re also given greater control over managing their money. This means avoiding credit card debt, and being responsible for their own financial wealth and wellbeing.

The impact is even bigger on businesses.
Companies like Afterpay are hubs of unparalleled consumer purchasing data. A nearly bottomless well of information on trends, habits and desires that power targeting, retargeting and micro-targeting.

⁽²⁾ https://www.campaignlive.co.uk/article/millennials-demand-instant-gratification-shaping-future-retail/1331511

⁽³⁾ https://www.fool.com.au/2018/02/22/afterpay-touch-group-ltd-just-grew-underlying-revenue-by-518/

Disruptive Intersections: The untrusting consumer

Building a detailed architecture to drive faster, stronger systems relies on a hard-to-determine data point - consumer trust.

Losing faith in institutions that were once considered the bedrock of society is fast becoming a staple behaviour of the modern consumer. According to the Edelman Trust Barometer (a global authority on public opinion and mood):

Only 37% of the general population say CEOs are credible ⁽⁴⁾

Trust in the four key institutions of society - government, business, media and NGOs - is at an all-time low ⁽⁴⁾

85% of respondents across demographics like education, pay rate, class and race lack belief that “the system” works in their interest ⁽⁴⁾

This feeling is expressed in reluctant, untrusting and unforgiving consumer behaviour against brands and businesses that appear to mishandle, mis-use or exploit their customers and their data.

As financial institutions learn more about their customers, they need to be more careful to uphold their customers’ trust. When consumers are already skeptical, losing trust is a huge risk to the financial industry.

⁽⁴⁾ https://www.edelman.com/trust2017/

Data Problems: The responsibility of knowledge

A.I. and data is a double-edged sword for the finance industry.

Every piece of data gathered is a new opportunity for an error, a leak or a hack. The rate of data mischief and theft is rising almost as quickly as rates of profits brought about by A.I.

In 2017, the UK saw 2.9 million organisations hit with cyber attacks at a total cost of £29.1 billion ⁽⁵⁾
Cybersecurity Ventures predicts global cybersecurity spending will exceed $1 trillion between 2017 and 2021. In 2004, the global cybersecurity market was worth $3.5 billion ⁽⁶⁾

Big Data brings bigger risks. Slip-ups can cause untrusting consumers to take their data elsewhere. That means one less point of information you can use.

⁽⁵⁾ https://www.itgovernance.co.uk/blog/2016-cyber-security-breaches-cost-uk-businesses-almost-30-billion/

⁽⁶⁾ http://www.wired.co.uk/article/job-security-cybersecurity-alec-ross

The Sensis View: Clean data is essential to an A.I. powered business

The Sensis View: Clean data is essential to A.I. powered business.

The potential of A.I. can only be unleashed through accurate data.

A key to building consumer trust is ensuring your data is accurate. Clean data is key to making accurate connections when you engage with your customers. No one likes to get ads for things they’ll never want or need, let alone receive an email addressed to the wrong person. But keeping up with consumers can be difficult.

39% of Australians have moved in the past 5 years ⁽⁷⁾
There are over 30,000 phone number changes each day ⁽⁸⁾
13% of emails in the average database are wrong ⁽⁹⁾

If you’re looking to take advantage, start with the basics and determine if your data is up to date. Check out our calculator tool to learn how much a small investment in data integrity can transform the way your business connects.

⁽⁷⁾ Source: ABS Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016

⁽⁸⁾ Source: ABS Trends Report 2010, Sensis Internal Reporting

⁽⁹⁾ Based on all Sensis Data Solutions jobs run in July 2017 - June 2018, Sensis Internal Reporting

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