January 30, 2018
While IBM outlines the future of identity in an extensive study of 4,000 adults across three regions, Canada is half-way in the future with a particular application – travel industry. With its Known Traveller Digital Identity concept, the government of Canada is testing a new airport security and screening system that will allow travellers to digitize and share travel documents & biometric information, and use an app to store & share that information with authorities in advance, allowing more time for pre-screening.
Key findings from the survey of 4,000 adults from across the US, APAC and Europe consumers include:
Security outweighs convenience: People ranked security as the highest priority for logging in to the majority of applications, particularly when it came to money-related apps.
Biometrics becoming mainstream: About 67% are comfortable using biometric authentication today, while 87% say they’ll be comfortable with these technologies in the future.
Millennials moving beyond passwords: While 75% of millennials are comfortable using biometrics today, less than half are using complex passwords and 41% reuse passwords. Older generations showed more care with password creation but were less inclined to adopt biometrics and multifactor authentication.
APAC leading charge on biometrics: Respondents in APAC were the most knowledgeable and comfortable with biometric authentication, while the US lagged furthest behind in these categories.
Additionally, the average internet user in America is managing over 150 online accounts that require a password, which is expected to rise to over 300 accounts in coming years.
Forecasts indicate that cross-border travel will grow by 50% over the next decade and reach 1.8 billion international arrivals by 2030. To take full advantage of the economic opportunities this increase in demand generates, stakeholders must confront pressures on the traveller journey, particularly the increased risk and related security requirements, as well as the limited growth capacity of travel-and border-related infrastructure.
This Known Traveller Digital Identity concept is founded on the principle that an individual traveller has control over the use of their own identity and its components. Due to this decentralization of control over the components of their identity, a traveller can push proof of their identity information – secured by distributed ledger technology and cryptography – to governmental and private-sector entities throughout their journey.
Access to verified personal biometric, biographic and historical travel data will enable entities along the way to undertake advanced risk assessment, verify travellers’ identities and provide seamless access through biometric recognition technology. All of this can be achieved without the need to have personal data stored in one central database, which would pose too great a risk for stakeholders responsible for securely handling personal identity information.
A working prototype of the concept demonstrating specific use cases will be showcased at the World Economic Forum Annual Meeting 2018 to policy-makers, technology innovators, and business executives. Moving forward, the project will seek to implement a scalable pilot of the Known Traveller Digital Identity with partner governments. – Liselotte de Maar, Managing Director, Travel Industry, Accenture Strategy, Netherlands
Here are four ways by which combining Big Data with machine learning has helped improve business intelligence:
1. Facilitating Customer Segmentation. In 2009, Orbitz created a machine learning team to facilitate segmentation, among other reasons. Three years later, it discovered a pattern from the data at its disposal: Mac users were willing to spend as much as 30% more per night for hotel rooms when compared to Windows users. This discovery made Orbitz swing into action and helped to lay the grounds for segmenting the business’s customer base based on the relative propensity to pay for varying hotel types.
2. Making Targeting Feasible and Effective. Google uses Big Data to better understand user preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query the user makes. This is why past choices end up impacting on some of the results the user is shown.
3. Fostering Predictive Analysis. Amex, the American Express Company, used Big Data to analyze and predict consumer behavior by learning from historical transactions. Through this, it was able to predict 24% of accounts in its Australian market that were about to close within four months. T-Mobile also uses Big Data to predict consumer fluctuations.
4. Providing Foundations for Risk Analysis and Regulation. American Express applies machine learning to analyze large historical datasets. The machine learning system is considered to differ from the previously existent fraud detection systems which included only manually created rules and is better off because it’s likely to improve with more data inputs. It also saves the company millions of dollars