The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

How Machine Learning Can Improve Your Business And Holiday Travel

February 10, 2016 at 4:06 PM

When you think of machine learning in action, it’s easy to imagine analytics-based marketing research or a matrix-style AI takeover. But there are actually far more grounded and practical applications that benefit hundreds of millions of people in their day-to-day leisure and business activities. Take the travel industry, for example. We’ve now reached a point in human history where over 100,000 flights are taking off every single day. With airports seeing more foot traffic than ever before – Dubai International alone saw over 78 million passengers in 2015 – they need to be run and managed like a carefully oiled machine. Alleviating bottlenecks is central to ensuring as smooth an operation as possible in the travelling experience.

In this blog, we’ll take a look at a few ways machine learning can improve travellers’ experiences and aid the industry as a whole in adjusting its operations to accommodate the new era of global travel.

Airlines and travel sites can help you plan the perfect trip

Nearly every organisation involved in the travel industry has been keeping records of its activities for decades on end – be it airline companies with holiday destination statistics or tourism agents with the popularity of certain seasonal attractions. Until fairly recently, all of this data had little use beyond fact-checking and surface-level insights.

With the rise of machine learning and predictive analytics, however, that data can be put to work to better align passengers with travel routes and amenities. Imagine planning a trip to Shanghai for a conference, but instead of having to wade through an endless stream of flight routes, hotels and activities for the trip, you’re immediately presented with options that are, statistically, best suited for you. It does this by looking at any trends in your previous flight info, such as a tendency to fly at night rather during the day, or to fly direct instead of having a layover. This is combined with information data from your online purchasing habits (those gold clubs you bought off Amazon, for example) to craft a customized trip that appeals to your hobbies and lifestyle. While it may seem eerie for a computer system to accurately predict a suitable trip based on its understanding of you, it’s only a few steps away from the technology behind automatically curated news feeds.

Read our blog post How Machine learning is placing Risk Managers on the Front Foot against Fraud.

Airport security can be less intrusive

So you’ve embraced the ease and convenience of an automated, customized itinerary, and are now on your way to the airport anticipating the arduous task of getting through airport security. But security measures really needn’t be as intrusive and time-consuming as they currently are. That’s the basic premise behind Silicon Valley-based Qylur’s  proposed solution – the Qylatron. It’s an automated luggage scanning device that uses machine learning technology to determine whether the contents of any particular bag are potentially harmful or illegal. To accomplish this, the machine uses X-rays and chemical screening to scan the contents of a bag and then compare each item to an internal library of prohibited objects and substances. The Qylatron is capable of learning about new prohibited items from its operators, as well as from other machines like it. The system can process ten pieces of luggage every minute, which is far quicker than the manual and often intrusive inspections conducted at most airports today. And considering the influx of travellers passing through airports on a daily basis, systems such as these can ease the massive backlogs and frustrations that often characterise the traveling experience for many.

You’re more likely to arrive on time

Perhaps the most important way machine learning can improve business travel and holiday travel alike is by predicting exactly when planes will take off and land – thereby reducing flight delays and runway congestion. It does this by analysing all the variables affecting departure and arrival times, like weather and possible ground issues, and then giving the prediction to technical staff at the airport to update details on all surrounding flights. Accurate prediction of take-off and landing is incredibly valuable in the airline industry, as it’s historically been difficult to account for variables in any meaningful way. This is reflected in the seven-minute average delay time when a plane is off schedule. Knowing when delays are likely makes it easier to avoid a knock-on effect on other flights. Time waiting at the gate is reduced for passengers and for the airlines, the cost-savings is huge: each minute in reduction per flight could also save $1.2 million in annual crew costs and $5 million in annual fuel savings for a midsized airline

Connecting the dots to further elevate the travelling experience

When augmenting travel information with data from hotels, restaurants, conference centres and the like, we start to uncover more opportunities to elevate their experiences by providing travellers with personalised suggestions on amenities, activities and leisure experiences. And thanks to internet ubiquity and mobile devices accompanying people wherever they go, reaching travellers to make personalised suggestions in real-time as they move from point A to B is a simple matter of following the crumbs of personal preference and past behaviour data they leave behind. The Global Travel Ecosystem from Amadeus is a great example, as it uses GPS and real-time algorithms to turn a traveller’s journey into a contextual and tailored experience – from personalised flight and hotel recommendations to suggested leisure activities and shopping. Travel services providers in turn benefit from increased conversion on complementary services or product offerings.

Using machine learning in business - download guide

Image credit: https://www.travelocity.com/

Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

Latest Posts

The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.  The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy.   Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

Collections Resilience post COVID-19 - part 2

Principa Decisions (Pty) L

Collections Resilience post COVID-19

Principa Decisions (Pty) L