The Data Analytics Blog

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

All Posts

Developments You Need To Know About In Machine Learning & AI

May 16, 2018 at 7:40 AM

During the last year, we’ve experienced the escalation of social issues around artificial intelligence (AI), with Elon Musk leading the charge. Musk continues to advocate the idea that humanity is getting closer to a Skynet-like future – to many people’s concern. One of the very real and valid concerns is the idea that many existing jobs will be automated, thanks to AI.

But despite these two major concerns, there continues to be significant innovation and advancement in the field of machine learning (ML) and AI. And perhaps due to these concerns, there has been a significant focus placed on the transparency of machine learning and AI algorithms and advancements. This gives us more insights into what's happening in the field, but many breakthroughs are not realised in the real world as they might cross the ethics line. In this blog, we'll discuss some of the latest advancements in the field of machine learning and artificial intelligence and what these developments mean for our future. 

AlphaGo Zero

In a paper published in Nature on 18 October 2017, Google’s Deepmind demonstrated the ability to master the game of Go, without human knowledge.

A bit of background

AlphaGo, the original, was the first computer program to beat a professional human player at the ancient Chinese game of Go. Following that, AlphaGo continued a winning streak to become the strongest Go player in history. 

So what is AlphaGo Zero?

AlphaGo Zero is similar to the original version. The difference between the two is twofold:

  1. Whereas AlphaGo was taught how to play the game of Go using thousands of human amateur and professional games, AlphaGo Zero was not taught. Zero learned to play the game entirely on its own, playing games against itself, starting with entirely random play and developing tactics as it learnt.
  2. AlphaGo Zero surpassed AlphaGo as the strongest Go player within just 40 days.

Why is this important?

The team at DeepMind, the world leader in AI research and the team behind AlphaGo, believes this has the potential to facilitate major scientific breakthrough, drastically changing the world for the better.

AlphaGo Zero starts from a blank slate and learns for itself, without any human intervention. This is important because this can be applied to any other domain. If you have a computer that can learn based on zero data from the start – only logic and basic rules, the application of this computer is limitless. This is very important in situations where human knowledge is expensive, unreliable or simply unavailable. The DeepMind team are looking towards similar structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials.

Read more on AlphaGo Zero here.

Self-driving Cars

Self-driving cars are the future we've all been looking forward to and the application of AI that seems the most promising (and most imminent). But with two fatal crashes involving two leading companies in the field occurring within a week, you've got to be concerned.

What happened?

Uber had been testing their self-driving cars in Arizona for between 13 and 17 months, when on 18 March 2018, one of the self-driving cars was involved in a fatal crash. By law, the self-driving car needs to be accompanied by a safety driver, who was indeed present in the car, but who failed to take action. The vehicle was travelling at 40mph or 64kph at the time of the crash. Footage released showed a woman crossing the road, who the car's sensors failed to pick up. The accident occurred after dark, in Tempe, Arizona.

A week later, on the 23rd of March 2018, another fatality made news when a driver of a Tesla Model X crashed in California. The car was operating on Autopilot at the time and had given several visual and audio warnings earlier on in the drive, but no intervention was detected from the driver. 

Does that mean we shouldn’t trust self-driving cars?

Not at all. But many companies are currently investing a lot of resources into developing a self-driving car, and all of these companies are looking to be the first to release a fully autonomous vehicle. History has taught these companies that whoever makes it to market first, is likely to gain a monopoly in what will be a lucrative industry in the future. (Click to Tweet!)

In this innovation race, and with so many companies talking about what is possible – it’s very easy for us to confuse what we currently have available with what is still being developed. Yes – what we've achieved thus far is impressive, but we should be careful of overestimating the technology that has been approved for public use and not confuse it for what has been achieved in labs and controlled and secure environments.

For example, while the Tesla autopilot feature is revolutionary and well-worth the hype, the company has said its Autopilot feature “can keep speed, change lanes and self-park but requires drivers to keep their eyes on the road and hands on the wheel, to be able to take control and avoid accidents”.  Although being the frontrunner has its advantages, being ahead of the pack in this area is a big risk as a lot of the legislation and ramifications for “failure of the AI system” is untested in court and may result in significant financial consequences.

GDPR

I'll bet you didn't think the GDPR has anything to do with ML or AI? It does, and with the GDPR coming into effect towards the end of May, it's very pertinent.

What is the GDPR?

Some South-Africans might not have heard about it, as it is an EU law. The GDPR, or General Data Protection Regulation, is new data protection and privacy law, adopted 27 April 2016 which becomes enforceable on 25 May 2018.

You can read more about the GDPR here, but your business should be making these changes if you are storing any data on European citizens and residents.

What does it have to do with ML or AI?

The new law governs how companies like Facebook gather data on people residing in the EU, including facial biometric data. These companies now require consent to collect data in any way or to use facial recognition on photos.  Many businesses are reviewing their data policies, security systems and products that are under development based on these regulations.

Beyond this, the GDPR refers to the use of AI and ML specifically in article 22(1).

“Any person has the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.”

What does this mean for South-African companies?

Nothing – yet (unless your data contains information on EU citizens). But it’s good to be aware of, as many have touted the GDPR the model on which all countries’ future data protection laws will be built.

And there you go – the top three developments in the AI and ML space you should be aware of. If you’re looking to keep up to date with South African insights into AI, ML and data analytics applications, subscribe to our blog notifications. We’ve also recently published a guide to using Machine Learning in your business, which you can download below.

Using machine learning in business - download guide

Robin Davies
Robin Davies
Robin Davies was the Head of Product Development at Principa for many years during which Robin’s team packaged complex concepts into easy-to-use products that help our clients to lift their business in often unexpected ways. Robin is currently the Head of Machine Learning at a prestigious firm in the UK.

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