EY acquires Applix, one of Italy's fastest growing digital scale-ups

18 December 2017 Consultancy.eu

The Italian arm of EY has acquired Applix, an Italian scale-up that designs and develops mobile solutions for multinationals and mid-sized companies. Applix has been rebranded as EY-Applix, similar to how The Parthenon Group was rebranded as Parthenon-EY globally and, in Germany, Innovalue as EY-Innovalue.

Applix has since its founding in 2010 become a household name in Italy’s bourgeoning startup and scale-up scene, having grown from a two man show – co-founders Claudio Somazzi (CEO) and Marco Cirilli (CTO) – to a company with currently over 100 employees across offices in Europe, America and Asia. The firm’s rapid growth has seen the Cagliari headquartered company recognised as one of Italy's fastest growing digital startups, and scale-ups later on in its journey, by a variety of Italian analysts and platforms. 

The private equity backed firm – in 2011 Applix became the first Italian startup in history to close a fund that raised over €4 million – made global headlines when Apple’s CEO Steve Jobs, during a keynote presentation for the iPad2, referred to Applix’s ‘Virtual History Rome’ app, today still one of the most successful Italian apps of all times. Applix’s apps are downloaded in more than 120 countries, with top 10 download positions achieved in over 50 countries. The firm’s success is, according to its clients, built on a proprietary approach for mobile user experience and user interface, as well as a solid track record in the build and delivery of solutions.

EY acquires Digital Transformation specialist Applix

With the acquisition of Applix, EY, an international accounting and consulting firm, aims at ramping up its position in the market for digital transformation, focused on the opportunities offered by the “mobile economy”. The two parties were at the time of deal closing well familiar with one another – over the past year, Applix has been involved in the implementation of several digital projects for EY customers, said Andrea Paliani, Head of Consulting at EY Italy. “The integration of Applix into our organisation is the natural conclusion of this business incubation process,” he remarked. 

Paliani: “The joining of forces brings additional, unique and differentiated skills to EY, which allow us enhance our digital strategy and execution services, in every business sector.” It will in addition place the firm “at the forefront of Italy’s professional services sector in terms of Open Innovation,” he added. Applix has a large design and development base in Sardinia, where it, in an open and collaborative environment, works closely together with designers, technology experts, business representatives and academics from the University of Cagliari to uncover and unleash the power of digital-driven innovation.

Claudio Somazzi, founder of Applix and now a Partner at EY in Italy, stated, “The integration of our team into EY is the logical conclusion of a seven year growth process. Under the wings of EY, EY-Applix will have the opportunity to work with international clients and co-create with them an innovative and concrete digital transformation process. This will allow us to serve a larger and more complex market, while helping us make a leap in the quality and breadth of our offerings.” 

Earlier this month, Parthenon-EY acquired the German team of OC&C Strategy Consultants, months after it also bolted-on the French and Benelux operations of the UK headquartered strategy consulting firm. Last year, EY acquired, among others, NeriWolff in Italy, financial services consultancy Innovalue in Germany and A-THREE in Belgium.

AI can improve operational risk management in banking

17 April 2019 Consultancy.eu

Risk management is an integral part of banking. By taking financial risks, banks are able to generate the profits that are necessary to survive. Risk management aims to control this process by making potential losses more predictable. This makes the bank more robust to external fluctuations.

Whereas profits can be made by accepting certain financial risks, operational risk is intrinsically different. Operational risks only cause losses – financially in terms of bottom-line impact and non-financially in the form of for instance reputational damage. The consequences of operational risk events can have a large impact on an organisation and the financial system as a whole as experienced during the last financial crisis. It is not surprising therefore that operational risk is receiving more attention within the financial sector, with banks trying to minimise the operational risks they take, given the resources available while keeping in mind the strategic goals of the organisation.

In the past decade, there has been major progress in the development of artificial intelligence (AI). AI algorithms excel at data analysis and have evolved to the point where they surpass human performance for a wide variety of tasks. More and more businesses exploit these technological advances to optimise different kinds of processes such as marketing, sales and e-commerce, manufacturing and logistics. In today’s growing data-driven world, this trend is expected to continue on the back of widening opportunities for use cases, including in the area of operational risk management.AI can improve operational risk management in banking

Challenges in operational risk management

In June 2011 the Basel committee published the Principles for the Sound Management of Operational Risk (BCBS), which provides a framework for the development of proper operational risk management. Three years later, a survey was conducted to measure to what extent banks complied with these principles. One of these principles states that banks should write a risk appetite and tolerance statement. Banks reported that this is more challenging for operational risk than for other risk categories and attributed this to the nature and pervasiveness of operational risk. The banks that did comply with this principle often reported the use of backward-looking metrics of operational risk, such as operational losses as a percentage of gross revenue.

The above example underlines the challenges which banks face in the management and measurement of operational risk. Compared to financial risk, operational risk is a more qualitative field of study. Whereas financial risk management has been the main priority of banking for a longer time, operational risk management is much younger resulting in less extensive historical data. Predictive modeling becomes more of a challenge in this situation.

On top of that, the events in operational risk are much more diverse in scope. The Basel committee defines operational risk as the “risk of loss resulting from inadequate or failed internal processes, people and systems or from external events”. Internal fraud, data leakage and reputational damage are very different problems, yet can be very closely related as well. A lot of the data in operational risk consists of textual input which contain qualitative information. The qualitative nature of operational risk is reflected in the Basel framework, which encompasses guidelines for organisational structures, culture and awareness, and qualitative reporting.

The computer as a reader

Artificial intelligence could play a valuable role in upgrading operational risk practices, with in particular machine learning – the field of self-learning computer algorithms – showing promise. Machine learning algorithms can make predictions based on data fed to the algorithm. Recently, major progress was made in the field of natural language processing (NLP). NLP focuses on using textual data for predictions. As an example, an algorithm can learn to rate hotel reviews. By processing large amounts of reviews together with their given ratings, the algorithm can learn to give a rating to a new review it has never seen before.

“There are many opportunities for operational risk management to exploit AI and other related technological advances.”
– Lars de Ruiter and Matthias Geerse, Solid Professionals

In operational risk, many textual reports are written regarding specific risks or risk appetite of the organisation as a whole. These reports are generally written by risk managers – experts in their field. Assuming that risk reports hold information which is absent in historical loss data, this information could be extracted and used for predictive purposes. So why should computers perform this task instead of risk experts? The answer lies within the physical limitations of human being – as well as the natural biases they have to cope with. While humans may struggle to remember a piece of text, a computer algorithm easily processes thousands of books and finds structure within using statistical methods with near-perfect precision.

Towards prediction of operational risk

The performance of NLP algorithms using word embeddings has increased tremendously of late. Using the embedding as a starting point, these algorithms learn the meaning of full sentences and use this information for further predictions. This is more generally called sentiment analysis. A popular application in the financial setting is predicting stock price movement from news articles, or twitter feeds. A recent paper by Denmark’s national bank predicts corporate distress of firms by analysing their annual reports. Taking textual data under scrutiny can make a contribution to quantitative predictive modelling, even in the financial sector where the use of words is very different to day-to-day language.

In summary, there are many opportunities for operational risk management to exploit AI and other related technological advances. One possibility is the classification of risk events. As an example, a computer algorithm can read risk descriptions written by risk managers and classify them according to their impact and frequency. Combining loss data with risk reports, improved prediction of risk events might lead to more accurate prediction of future losses. A different application is the measurement of more abstract concepts such as the financial health or maybe the cultural aspects of a company.

An article by Lars de Ruiter and Matthias Geerse. Both are finance & risk consultants at Solid Professionals, a consultancy from the Netherlands.