Most of us have taken personality tests for a job application. However, few people realise that psychometrics, the science behind them, is a robust academic discipline with a heritage going back to one of the finest scientific minds of the 19^th Century.

Psychometrics is much more than a recruitment tool. It is the measurement and quantification of psychological processes, from personality factors and cognitive abilities to cultural values etc. It is premised on the understanding that human behaviour is measurable and predictable.

Researchers have laboured for many decades to create a reliable means of measuring psychological patterns. Psychometrics has evolved as an empirical, data-driven science able to model human behaviour.

Reason 1: Established Science

This is not a new area of study. The Psychometric Society, which advances quantitative methods in behavioural science, was founded in 1935. The origins of the science can be traced back much further, to the 1860s and the work of Sir Francis Galton, half-cousin and friend to Charles Darwin.

Today, a large research community is engaged in the discipline, producing a growing body of highly credible scientific evidence to support the use of psychometrics across various sectors. Universities have entire research labs dedicated to psychometrics and quantitative psychology. Such institutions include The Psychometrics Centre at the University of Cambridge, which researches the measurement of human behaviour using pioneering techniques and diverse sources of data. Psychometricians research the development of classical paper-pencil questionnaires and computer adaptive tests. More recent research has focused on the use of digital footprints to predict psychological traits.

Reason 2: Rigorous Testing and Development

Psychometricians follow a rigorous scientific process, undertaking detailed statistical analysis and meeting numerous stringent evidential criteria to prove that tests are both valid and reliable.

Some tests, like the International Personality Item Pool (IPIP) scales which measure respondents against the Big Five personality scale (also known as OCEAN, for Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism), have evolved through constant testing over decades. For example, the IPIP Neuroticism, Extraversion, Openness Personality Inventory (NEO-PI) assessment has been revised for well over 30 years across various populations and in many language versions.

Psychometric test construction includes:

• Item development procedures are designed to confirm the relevance of each question in a test and eliminate bias. They follow an iterative process and can sometimes take years.
• Validity assessments assess the effectiveness of the test in measuring what it was designed to measure (or not). E.g. Concurrent validity examines the accuracy of the test in predicting related future outcomes, such as the accuracy of a cognitive ability test in predicting academic outcomes.
• Reliability analyses assess the stability of the test through retesting procedures.
• Standardisation and norms allow test results to be comparable across different populations, where necessary and possible. Usually published tests are revised when new data becomes available.

A further challenge is ensuring that psychometric assessments safeguard against inauthentic results, whether attempts to game the system or provide answers preferred by the assessor. Attention checks can help by looking for patterned responding or measuring response times for different questions. Inconsistency in test responses is generally an indicator that the test responses are not an accurate representation of the individual, whether due to subconscious biases or fraud, at a more conscious level.

Reason 3: It Works

Well-established research confirms that these tests can be applied across many fields. In education, for example, measuring aptitude has proven to help students reach their full potential. In recruitment, the all-round objective view of a candidate’s suitability allows employers to relate applicants’ cognitive ability score to their work performance, or their integrity score and likely absenteeism as an employee.

More recently, digital footprints have been used to accurately predict a range of insightful attributes. Researchers used Facebook ‘Likes’ to predict the extent to which a person is naturally organised, reliable, and consistent; or one’s predisposition for depression; amongst a range of other features.

If as many say, the modern age is the information age, then expect to see a lot more psychometrics in the future. While most data sources tangentially capture an aspect of human behaviour, modern psychometrics provides the whole picture.

Practical Usage

Advancement in this field has not been achieved solely within academia. Commercial entities are driving the use of psychometrics in the pursuit of profit in ways that go beyond the recruitment of staff.

Investment firms are using these tests to establish the risk a customer is willing to take to make better investment selections. While in credit risk, lenders are turning to psychometrics to help them augment traditional credit scoring methods as they find that integrity and conscientiousness (for example) are closely linked to a person’s willingness to prioritise debt and thus avoid financial difficulty.

In a world in which communication is increasingly targeted to precise demographics, some commercial adopters use psychometrics to ensure a close personality fit between their customers and their products.

It is an exciting time in the field of psychometrics. The science is evolving fast, new data sources are opening up and the practitioners themselves are constantly refining their instruments. Some people are quick to dismiss psychometrics but it is a real science broadening our understanding of people and their behaviours.

5th June 2017

Today is a big day for me, and it is appropriate that this is my first ever Linked In article. Today marks the third anniversary of the day I packed my bags and upped sticks from Dublin to London. This has come around far quicker than I expected, and, to be honest when I moved I only ever intended to ‘do’ three years here, but here I am – still going strong.

The three-year anniversary is quite a big one for my fellow ex-pats here as it marks the date when we can FINALLY apply for credit with any real chance of success! Before now, I’ve been classified, in my mind incorrectly, as being a “thin-file.” In the common tongue, this means that the relevant credit bureaux here in the UK do not hold enough data about my financial history to return a thick file to lenders, insurers or anyone else who asks for it, in this case, due to less than 3 years address history.

What happens when a lender’s decision engine gets a thin-file back from a bureau? Its algorithms crunch some numbers, and it spits out a decision: thin-file = no credit. It is quite literally a case of “COMPUTER SAYS NO”. Of course, there can be some situations where it is perfectly acceptable for the computer to say no, for example where the applicant’s historical data indicates a pattern of default, or an inability to meet repayments on time. However, my challenge is far more basic.

As it stands for people like me, we can’t even get that far into the process, because most of the credit application forms require a three-year address history to do the search.

So the applicant is left with a choice – abandon the application? Or say that they have lived here for more than three years? Well-intentioned applicants may be tempted to do just that and stretch the truth about their circumstances. While this, of course, is never acceptable, the fact that otherwise honest people can be driven to such lengths shows how inefficient the system is. This may apply to something as mundane as trying to get a mobile phone or broadband contract.

Three years ago I was amazed at how difficult it was to do simple things like open a bank account. I’m fortunate in that both companies I’ve worked for in the UK have been very diverse places with lots of nationalities represented.

I was a part of a big hiring drive at Zopa, my first employer in London, and there were four others facing the same challenge as me: “how do we open a bank account?’. Luckily for me (and credit to them), HSBC were excellent. They understood the problem and managed to work through it. However, one of my colleagues, a fellow Irishman, was not so lucky with another bank and described the process as like ‘pulling teeth.’

Hitting the three-year mark is great as I am now past the first hurdle and can apply. And as I’ve managed to get some credit over the course of the last three years and I managed it responsibly, my credit file should have lots of lovely data so from now on things should get easier. But I have to ask, why have I had to wait three years? Why all the hoops I had to jump through?

In Ireland, I had a credit card from the age of 18. I’ve had a car loan, which I repaid two years early. I’ve had overdrafts, phone contracts and more. Why, when I moved less than 300 miles across the Irish Sea, did this data disappear? I appreciate that Ireland does not have exactly the most developed credit bureau in the world but still, I think it’s a valid point.

So what can be done to help the ‘new-to-country thin-files’? Fortunately, I can directly help fix this problem. I get to do something that can make this better.

Coremetrix has proven that there is a link between someone’s personality and their credit intent. By marrying an innovative visual quiz with a robust, statistical model, we can add a lot more data to allow a lender to make a decision.

There are a lot of people out there who are highly qualified, in good jobs, with good salaries and who would be worthy borrowers but as they are new to a country and have no historical data, computer says no!

In the Coremetrix world, the first computer (using the historical data) can say no. But it will then refer the applicant to undertake the psychometric assessment. Once Coremetrix computes a score, it’s then returned to the lender, and they can make a new decision.

Maybe it will still be a no for some, but many more deserving people will get a yes!

Critically, we have proved that we can do this without increasing the default rate in the portfolio and that the psychometrics-accepted population would perform in line with, or in some cases outperform their thick-file counterparts.

My thin-file experience was in the UK, but we can deploy our product anywhere in the world.

We have working models in the UK, Slovakia, Poland, Turkey, South Africa, India and are developing them in many more new territories. We localise the quiz to account for language, culture, sensitivities and understanding of imagery. The models are built for specific regions accounting for the target population.

There are many more use-cases, and we have a mission to bank the unbanked, but today especially I am reminded of the challenges of being new-to-country and a thin-file! But change is possible! It’s something I feel very passionate about, hell it’s why I joined the company! Can we get credit? To quote Barack Obama – “Is feider linn!”

Conor Redmond

Head of Operations

Does money explain success?

Money talks in the world of sport. It buys the best players, the best management and the best facilities – the best of everything necessary to ensure success.

The world’s top revenue-generating teams are certainly a glittering array of household names, including illustrious brands across major sports such as Manchester United, the Dallas Cowboys and the New York Yankees. According to Forbes, between them, the top 10 are worth just shy of $6.8 billion.

Yet, in the past year, the sporting world has demonstrated several times that money doesn’t always equal achievement – Chelsea FC won the English Premiership; Monaco won the Ligue 1 in France, and Chicago Cubs won the World Series in baseball.

None of these clubs, of course, are poor, but they all outperformed teams with greater financial resources – teams which on paper were stronger. How did they do it?

Chelsea Football Club: 2016-2017 Premiership title

The focus of this piece is Chelsea’s victory. Despite presenting a very similar line-up of players to the one that got them to 10^th place a year ago, they topped the league and almost doubled their points from last year (93 versus 47).

The difference was smart management and an understanding of how group dynamics and processes can transform performance – factors that are as applicable to business as they are to sport.

Group roles, clarity and acceptance

Clear group roles are key: everyone knows what to expect from medical staff, managers, defenders and strikers, for example, and all organisations should aim for a similar level of clarity. In addition to role clarity, successful teams also exhibit acceptance: the responsibility of those who assign tasks to ensure that individuals accept the expectations associated with their roles. Should one of these conditions be absent, teams risk both individual and team underperformance.

The triumphant Chelsea team offers examples of both role clarity and acceptance: Victor Moses, a career striker, was required to play this year as a wing-back, while Cesc Fabregas had to give up his status as a first team player and get used to coming off the bench part way through each match. Both made a success of their new roles, suggesting that the manager, Antonio Conte, ensured that both understood and accepted the expectations placed on them.

Productive group norms

Group norms are behaviours, beliefs or performance standards that can be either formally or informally developed by a group. Examples might be modes of address, communication channels (email versus face to face, for example), punctuality or dress codes. Positive norms represent one of the structural characteristics that make a group of individuals become a functional team, with those norms enforced naturally by the power of social pressure.

Some teams adopt higher standards than others of course, and the winning Chelsea team were held to high standards by two of its leaders: Conte, who used the word “work” or a derivative 32 times in under an hour in an interview with the Guardian, and Eden Hazard, one of Chelsea’s and the world’s most talented players, who said “very hard” training was key to the team’s success. For Hazard, the key factor was not the performance of opponents or Chelsea’s superior match performance, but a behavioural standard entirely under the team’s control: their hard work on the training pitch.

Cohesion

Cohesion has been defined as “a dynamic process that is reflected in the tendency for a group to stick together and remain united in pursuit of instrumental objectives and/or for the satisfaction of member effective need”. This definition implies that individuals work and remain united for two main reasons: task cohesion (to achieve common goals); and social cohesion (satisfying a need for affiliation).

Some researchers have concluded that the relation between cohesion and performance is circular – that the more a team is cohesive the more likely it is to achieve success and that the more successful the team, the more likely it is to be cohesive.

In an Evening Standard interview, defender César Azpilicueta talked about Conte’s insistence on the team “being united on the pitch”, building “the identity he wanted” and his defence colleagues’ “different qualities that complement each other”. Clearly, Conte built a strong and cohesive team, united in the pursuit of two instrumental objectives: being tactically perfect and winning the championship.

Changing perspective

The lessons for teams in a non-sporting context are – I hope – clear, but this piece does not intend to explain all of the factors that contribute to team or organisational success. It was written to inspire and encourage group leaders, managers, employers and employees to think from a different perspective.

Focusing on strategies to improve understanding and acceptance of roles, to set and maintain positive company norms and encourage group cohesion could give your organisation the edge to outperform bigger or stronger competitors.

In my own organisation, we see the reality of these observations daily. Coremetrix helps lenders and insurers find new customers through the use of psychometric data, making up for deficiencies in their credit history. This offering means that we have a very desperate team, composed of academic psychologists, experienced risk management professionals and software specialists.

The people in this diverse group all have very well defined roles but acceptance is key – especially when people with different specialisms are working together on a project. We are also all united by a common vision, which is to help our clients broaden their customer base safely and work towards a world where everyone – regardless of age, location, gender or credit history – has affordable access to the financial services they need. Team psychology certainly works for us, and I’m sure it would work for you too.

Author: David Kaufer, Senior Research Psychologist

‘Design Thinking’ is an observational method used by businesses to analyse, assess and solve problems. Practitioners do not have to be designers but should bring a designer’s attitude to their work, or at least an inclination to change.

From IDEO’s founder “fail early to succeed sooner” to Guy Kawasaki’s “don’t worry be crappy”, ‘Design Thinking’ is based on three principles: ideate, prototype & iterate.

A great example of experiential innovation through ‘Design Thinking’ is represented by Bank of America’s collaboration with IDEO in late 2005.

One result of it, “Keep the Change”, is a saving account allowing customers to round up card purchases to the nearest dollar, automatically depositing the balance. Inspired by the habit of saving the change from cash purchases, the product indicates how design thinkers rely on customer insights gained from real-world experience, not just historical data or market research. In less than a year the programme that followed this simple insight gained 2.5 million customers. Enrolment now totals more than 5 million consumers who, together, have saved more than $500 million.

‘Design Thinking’ taps into our emotions and aims to deliver innovation beyond aesthetics, to meet both our needs and our desires. Products need to appeal both emotionally and functionally.

As Jay Samit explains in “Disrupt yourself”: “Failing to create the complete ecosystem, of which the product is just a part, would result in the failure of the product itself”.

Sony experienced just this outcome in 2005 when it launched Librie, its first digital store. They were among the first to understand the consumer appetite for a digital reading device but failed to design all the services around the product, seeing it purely as a piece of hardware and, leaving the titles to publishers. By the time Sony realised the mistake, Amazon had released the first Kindle, which sold out in five hours.

In the same way, the Kindle wasn’t the first eBook digital reader, Apple’s iPod was not the first MP3 player, but as Guy Kawasaki said, “it was the first to be delightful”.

Once technological feasibility and market viability are met, a business must find desirable solutions for clients. Once a product is performing a task we then expect sophisticated experiences that are emotionally satisfying and meaningful. These experiences are the whole ecosystem in which the product will live.

The added value of ‘Design Thinking’ is the “empathy path”, placing the user’s benefit at the centre of the story.

The benefit of this is exemplified in the development of the ice business in the USA from the late 1800s, which came in the following three stages:

ICE 1.0: The ice harvester would wait for winter, go to frozen lakes and cut blocks of ice to then sell to clients.

ICE 2.0: 30 years later there was a major technological breakthrough: the ice factory, in which ice was made by freezing water and delivered via the ice man in the ice truck, with no more limitations from climate or season.

ICE 3.0: the introduction of the ‘personal chiller’: the refrigerator

This came in three discrete stages, through separate organisations ice harvesters did not become ice factories and the later did not enter the refrigerator business. This is because most companies define themselves by what they do, not the benefit they provide.

‘Design Thinking’ at COREMETRIX

Well most of us have been there! Our team is composed of professionals from all over the world and we have 11 nationalities represented. We have been where our clients’ applicants are. Having been ‘thin files’ for a while we can walk in our clients and client’s applicant’s shoes too.

In a dynamic society of ‘working-abroad’ and ‘forever-thin-file’ millennials, COREMETRIX provides a solution to lenders’ and consumers needs and addresses their frustrations by adding a new layer of information about creditworthiness. Consumers who were originally denied credit because of a lack of information, in spite of their financial possibilities, now have the chance to apply for a loan and get accepted based on their psychometric score.

With a psychometric assessment, we enable risk managers to take informed decisions based on data that refers to their applicants’ personality and financial attitude.

COREMETRIX is disrupting the way consumers’ access financial services, enabling lenders to make decisions based on more and better quality data and providing a benefit to both lenders and consumers.

In the end, when it comes to innovation it is quite easy to reach a conclusion: disrupt or be disrupted!

Caterina Ponsicchi, Creative Product Manager

New service offers personality insight for struggling borrowers

COREMETRIX, the world’s leading provider of psychographic data, has announced the launch of a new product allowing lenders and debt collection agencies to assess the likelihood of customers recovering after they fall into arrears.

The Collections Scorecard helps lenders differentiate between borrowers who have fallen into arrears but will recover and those customers who can’t pay or won’t pay, allowing them to determine the best approach to ensure that they recover their funds cost-effectively from individual customers.

As with all COREMETRIX services, the product is based on a robust sample of underlying data. The firm analysed around 5,000 credit card accounts, looking at early arrears – people who fell into difficulties within one to six months of obtaining credit – and performance six months later.

COREMETRIX’s data scientists focused on those who recovered, identifying the psychometric traits that helped them do so – variables like attitude to money, financial goals and commitment to the future. The resulting Collections Scorecard offers a predictive insight into how different borrowers will cope with being in arrears.

Clare McCaffery, Managing Director of COREMETRIX, said the Scorecard will help lenders and debt management organisations refine their collections processes and cut costs.

She said: “A single high-street lender can pass millions of pounds of non-performing debt on to debt collection agencies in a year. Each of those agencies’ agents will have a similar script and work to a uniform process. But as we know, every borrower is different and it’s both wasteful and unnecessarily distressing for consumers to be subjected to a ‘one-size-fits-all’ approach.”

The COREMETRIX Scorecard allows lenders to identify people in three broad categories: those likely to self-cure, those who need some help and consumers for whom the burden is simply too onerous.

Stephen Connolly, Head of Analytics at COREMETRIX and part of the team behind the product, said: “Any scoring mechanism that puts people through the right strategy is invaluable to lenders but up to now the data hasn’t been available to investigate the personalities of people in arrears. Our score gives lenders the confidence to select the right procedure for different people.”

While the Collections Scorecard offers meaningful cost savings for lenders, it will also perform an important social role. In addition to sparing responsible customers unnecessary official communications, it may allow lenders to identify customers with mental health problems, or those experiencing extraordinary stresses such as a bereavement or unemployment.

McCaffery added: “Our core mission is to help unlock financial services, at an affordable rate, to as many people as possible and this product is an extension of that. We want to help both lenders and consumers achieve the most favourable possible resolution to arrears situations. The scorecard is freshly out of the development stage and is already in beta mode with a client”.

At COREMETRIX we recently did some big changes to our architecture and this is where inspiration for writing this article came from. We are fortunate enough not to have to deal with a monolith application but rather with microservices. We run on the cloud (AWS) and we are big fans of automation, from infrastructure as code (Terraform, Ansible) to our tests (CDC test, synthetic monitoring). While a microservice architecture has a lot of benefits, as your architecture evolves you can end up with quite a lot of dependencies between your services.

This article will highlight some of the benefits of moving towards an asynchronous architecture and some of our learnings.

Synchronous vs. Asynchronous

A synchronous operation is when you ask someone else for something and you wait for him or her to respond. This operation occurs in actual time. As illustrated in the diagram below, after making a request, Alice has to actively wait for Bob to respond. Both Alice and Bob have to work together at the same time – in other words they are synchronised.

An asynchronous operation is when you send someone a message and you do not wait for him or her to reply. You can go on and do your business and only react once you receive a reply. One example of asynchronous communication is an email communication. The diagram below illustrates Alice and Bob working in an asynchronous way.

Microservice architecture

A Microservice is an architectural term that describes a method of developing software systems independently deployable and loosely coupled. Rather than creating a large application that does everything, or in other words a monolith, you create a suite of modular services, where each service has its own well-defined function. Microservices integrate via well-defined interfaces, most commonly REST over HTTP.

Another important aspect of a microservice is data encapsulation. Each service owns its own data and this data is only accessed via its interface.

I’ve definitely seen places where so-called microservices shared the same database. This is a major anti-pattern where you pay the cost of a distributed application and lose a lot of the benefits of microservice architecture. Going back and fixing things like this is time consuming.

A microservice architecture allows you to choose the right technology for the specific problem. It also means that they are easier to maintain as the codebase is a lot smaller.

Our journey started like this, we followed the best practises, some of them mentioned above, and we took into account things like the 12 factor app. We took tests seriously and introduced Consumer Driven Contacts (CDC) and Synthetic Monitoring. CDC tests were relatively simple to implement, where each test was written from the point of view of the consumer and how each consumer expected an API to behave. Tests created their own data and cleaned up after themselves.

We also made sure that we decoupled legacy services that were integrated at the database level. We end up with a microservice architecture where each service could be safely deployed multiple times a day. Each service owns only the data it needs to do its job.

As the application evolved, more and more microservices got created and introducing certain features became a bit harder as some of the services inevitably became quite coupled to one another. Further more, if one service became unavailable this immediately affected other services that depend on it.

The diagram below illustrates a simplified version of how the architecture looked at that point in time. You can easily imagine that if service A becomes unavailable for a period of time, service I cannot do its work.

Apart from the fact that a calling service is impacted by errors, we also lost some of the flexibility as every service knows about each other.

Asynchronous Microservice architecture

Introducing a message queue like RabbitMQ and making services interacting with each other this way solved a lot of the previously specified problems. The number of dependencies was minimised (less coupling), therefore each service became more autonomous. More importantly, a service may continue to work if any of its downstream dependencies are down.

In this new architecture none of these services know about each other. Also availability is increased. If you imagine that service I is a service that scores certain events and service A is service that produces these events. In case of service I becoming unavailable for a while, A can continue to function. In the meantime these messages get accumulated on the queue and when the scoring service becomes available again, they will be consumed.

This new set up comes with some new problems that you will need to take into account. Things like what happens if a message cannot be consumed? How do you make sure you do not lose messages? What if your message queue gets filled up? At Coremetrix, having chosen RabbitMQ as our message queue, we answered these questions by making sure every queue has an equivalent dead letter queue where rejected messages end up. These messages can be manually inspected and even replayed later on. We also made our queues mirrored across multiple nodes for high availability. And finally we set up alerts on queue sizes.

Another complication that we had to overcome was testing. Automated testing is a lot more problematic when it comes to asynchronous systems. Our tests consist of unit tests and integration tests that run against Docker containers and stubs when we do a build. On top of that we have CDC tests and synthetic tests after deployment to each environment.

One of the first things we had to do was to write integration tests for the integration with the queues. These tests give us the confidence that we are using the infrastructure correctly.

Secondly, after the new changes were introduced, the majority of our CDC test started failing. In these tests we had to replicate the behaviour of the producer and poll the downstream API to check for the response. Our test are written in Scala, you can imagine a typical test looking like something like this:

You would have to also set up some PatienceConfig to define the time you tolerate for unsuccessful attempts and the interval to sleep between attempts.

These tests give us the confidence that messages can be published/consumed and served as expected via the tested API.

Summary

Moving to an asynchronous architecture made our system more robust, less coupled and improved performance and scalability. It also improved reliability by making our system more tolerant to errors. There are definitely a lot of new things to learn and new problems to take into account. Testing can be more complicated but that is a price worth paying.

Cristian Pupazan

Head of Engineering

As we are all too aware, manifestos are a feature of modern party politics. They also have an application in the IT world, and while political manifestos are sometimes forgotten, their computing equivalents help software developers produce systems that are efficient, adaptable and robust, whatever the demands of users. Here, we discuss three of the most important.

Manifesto for Agile Software Development

This was released by 17 pioneering software professionals after a gathering in 2001. They came from different industries and backgrounds but unanimously agreed on four values

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiation

Responding to change over following a plan

The authors believe that there is value in the items on the right, but they value the items on the left more. The Agile movement these principles spawned has not only shaped the way software is developed but also transformed organisations of all kinds.

The term “agile” refers to the short feedback cycle. Prioritisation allows important or risky features to be developed, tested and delivered first. Agile became a key to innovation. The short feedback loop enables ideas to be tested, iterated, progressed, or even dismissed quickly. Being agile also means to adapt to changes in business rather than following the pre-defined plan.

At Coremetrix, we are continually reaping the benefits of applying the Agile manifesto. The tech team collaborates closely with our other teams, including data scientists, data modelling specialists, research psychologists and salespeople. By taking requirements, priorities and feedbacks directly from these stakeholders, we ensure that the right platform is developed for every project, from compiling the quiz to collating the data.

Manifesto for Software Craftsmanship

Agile was just the start. Many adoptions of that approach tend to focus on the process and ignoring technical practices and the competence of developers.  This can result in systems that are expensive to maintain and difficult to evolve. That realisation led to the evolution of a fifth Agile value: “craftsmanship over execution”. This, in turn, yielded another manifesto, encapsulating the following values:

Not only working software, but also well-crafted software

Not only responding to change but also steadily adding value

Not only individuals and interactions but also a community of professionals

Not only customer collaboration, but also productive partnerships

The manifesto emphasises that “in pursuit of the items on the left we have found the items on the right to be indispensable.”  The manifesto is about raising the bar of quality in the software profession.

Every member of the tech team at Coremetrix strives to be a craftsman and shares that mindset on professionalism in software development.  We promote well-crafted software as well as working software. We adopt relevant and important technical practices and strive to keep code quality high. We believe that each developer should not only improve themselves but also others in the company and in the wider tech community. We create simple, elegant and quality software solutions to deliver business value.

The Reactive Manifesto

The version 2 of the manifesto was formally published in 2014, which defines Reactive Systems as

Responsive: The system responds in a timely manner if at all possible

Resilient: The system stays responsive in the face of failure

Elastic: The system stays responsive to varying workload

Message Driven: Reactive Systems rely on asynchronous message-passing to establish a boundary between components…

This manifesto summarises a set of design architectural design principles that are discovered independently by organisations from different domains. The industry encapsulates in terms of the 4Rs stated in version 1 of the manifesto: react to users, react to failure, react to load and react to events.

The manifesto is created to address the challenges faced by modern applications. They must run on multicore processors, serving billions of requests each day (including from Internet of Things (IOTs) and mobile devices), keep low latency, high throughput and availability.

Coremetrix’s quiz and data collection platform consist of many microservices and software components. At the system level, the tech team applies these reactive design principles when we design, develop, deploy and continually evolve the platform. At each software component level, we use programming languages and tools, such as Scala and Akka, that support reactive programming. Hence, the platform, which is backed by an asynchronous messaging system (RabbitMQ) is deployed in the cloud and has all of the reactive properties mentioned above.

Summary

The Agile Manifesto mainly addresses the interactions between customers and developers so that the right software is developed. The Craftsmanship Manifesto is to raise the bar of the software profession so that systems are developed in the right way. The Reactive Manifesto gives guidance on designing systems so it can handle the business requirements in the age of cloud computing, big data and IoTs. Any business developing software to deliver competitive advantages must address the challenges and the aspirations indicated by the three manifestos.

Ex Coremetrix Senior Software Engineer

We’re often told that the UK has a credit problem – that we’re too reliant on cheap debt – but many lenders would say they have the opposite problem with their customers.

Recent research suggests that there is as much as £90 billion of unused credit in the UK: credit lines to which consumers are entitled but which they choose not to access. In many cases, they have used their “dormant” credit cards only once, or maybe even never.

Such borrowers are of course potential customers for lenders, but this unused credit is about to become a costly problem for banks. To explain, we’ll have to briefly enter the rarefied world of accounting regulation – IFRS 9 to be precise, which from January 2018 will require card issuers to make provision for a year’s worth of unexpected loss on credit accounts. For the first time, this will include unused credit lines. For the highest-risk customers, banks must hold enough capital to account for lifetime losses.

This will be a costly requirement but it could be just the push banks need to either retire inactive accounts or galvanise their relationships with dormant customers.

My own experience is that they’re not great at the latter. I have a current account, a savings account, my mortgage and an ISA with one major UK bank which persists in offering me financial products that I simply do not need. What’s particularly frustrating about this is that it has all the information at its disposal to tailor its offering precisely to my lifestyle and financial requirements.

As banks decide how to solve the problem of unused credit, they should look to what they already know about their customers. But to really get this right they need an insight into what drives borrowers’ behaviour.

Psychometrics – the measurement of personality, values, aptitude, opinions and other traits – offers lenders a direct, predictive window into the impulses governing their customers’ financial decisions.

At one end of the spectrum, psychometric testing can identify the kind of customers lenders simply don’t want – irresponsible people who do not prioritise their obligations. In an environment when unused credit in itself is expensive, regardless of loss, identifying this cohort is critical as banks rebalance their customer base.

But more intriguing is the capacity of psychometrics to help credit providers re-energise their relationships with the customers they do want, by giving them the insight to offer products of direct relevance. Conscientious people might respond to a bank offering products tailored to home improvements, for example, while adventurous types might respond well to offers related to travel.

Psychometric testing offers exactly this level of perception about the customer base. All borrowers need to do is take a quick image-based test, provided by the bank, which can then use that information to offer a highly tailored product set to individuals.

Lynsey Hoxha, Business Development Director at COREMETRIX

After a huge amount of work from our local engineering team, with the assistance from our colleagues in Creditinfo Global Technologies, Coremetrix are delighted to confirm that we have completed our migration to a containerised infrastructure leveraging Kubernetes.

Kubernetes is a natural technology choice for Coremetrix and allows us to maximise the advantage we’ve obtained through running our services in the AWS cloud. Kubernetes means that our clients can rest assured that our services will scale horizontally no matter how much load they are put under and will further reduce the possibility of any downtime on the platform.

It is critical to Coremetrix to provide a global solution that will scale massively while keeping our technology operating costs as low as possible, Kubernetes gives us the tools to do this and provides the stability you would associate with a large enterprise combined with the agility and flexibility of a startup.

Head of Engineering, Cristian Pupazan led and is delighted to complete the migration; “It is important that our quiz platform can run across multiple operating environments such as private clouds powered by opensource technologies or public clouds such as AWS. Moving to technology such a Kubernetes enables us to achieve this. Also using Kubernetes means that our deployments are now a lot cleaner and our services are self-healing and easy to scale. Building on top of such a platform will give the tech team more velocity, efficiency and agility.”

Well done to Cris and team!

According to Conor Redmond, Head of Operations at Coremetrix, the new infrastructure improves on an already impressive offering:“Our clients rely on us to provide innovative solutions that scale. Kubernetes allows us to meet our service requirements to them and to provide a highly stable infrastructure. We already provide an uptime SLA of 99.99% so our new infrastructure can only provide even greater confidence and better service. Most importantly for us as a growing business, it also allows us to maintain our low-cost base while having the confidence that we can scale without limits as we grow.”

Some info below on Kubernetes and containerisation.

https://kubernetes.io/…/concep…/overview/what-is-kubernetes/
https://www.cio.com/…/what-are-containers-and-why-do-you-ne…