Accenture acquires SEC Servizi from Intesa Sanpaolo

05 December 2018 Consultancy.eu

Accenture has acquired SEC Servizi, an Italian company that provides technology services and software applications to financial institutions. The firm has purchased 80.8 percent of the shares from parent banking group Intesa Sanpaolo, and is on the verge of acquiring the remaining interests in SEC Servizi Spa held by other shareholders.

Mauro Macchi, Head of Accenture's Financial Services division in Italy, said that the deal is part of the firm’s ambition to become the market leader in consulting and technology services to players in the financial services sector. According to the latest estimates from Assocconsult, Italy’s association for management consultancies, financial services consulting generates revenues of €1.1 billion, representing around a quarter of Italy’s consulting industry.

Padua-based SEC Servizi employs nearly 400 people, and serves some 35 banking customers and other mid-sized financial institutions in the country. According to Macchi, the acquisition will enable Accenture to create an “advanced and innovative core banking platform that can support banks in their transition to digital”. He explained: “Technological innovation is becoming an increasingly central element for banks. While it for long was primarily geared towards customer services, it now becoming central to strategies, operating models and addressing internal change.”

Accenture acquires SEC Servizi from Intesa Sanpaolo

As part of the integration, Accenture will maintain SEC Servizi’s brand and take over the end responsibility for serving the firm’s customers, including Intesa Sanpaolo. The Italian banking group, with assets under management of approximately $800 billion and 11.9 million customers the largest in the country, acquired SEC Servizi last year as part of the acquisition of certain assets, liabilities and legal relationships of Banca Popolare di Vicenza and Veneto Banca – with both banks entering compulsory administrative liquidation. The integration of SEC Servizi into Intesa Sanpaolo however was not a smooth one: in November 2017 SEC Servizi’s staff at the Padua office went on strike for a few days, which affected the banking system in northern Italy, and in recent months staff remained unsettled due to rumours that their job outlook may be impacted on the back of reorganisation plans from Intesa Sanpaolo.

Macchi highlighted that for banking groups across the country, embracing digital is essential to stay ahead of the curve. The banking world is grappling with disruption, spurred by changing customer demands, the rise of emerging technologies, competition from innovative players such as FinTechs but also incumbent technology giants such as Amazon and Apple, and a growing regulatory burden. According to Accenture's Head of Financial Services in Italy, digitisation has now reached its point of no return, with the need to embrace technology to coincide with an increasingly open approach to innovation and collaboration. “We are witnessing a growing climate favourable to the formation of ecosystems, alliances and partnerships in search of new frontiers of efficiency, competitiveness and value creation.”

“By creating an innovation hub in Italy, we can support banks with building their digital maturity, enabling them to compete with FinTechs and digital native players like Amazon and Google.” 

In other deals in Italy’s consulting landscape, Big Four firm EY last year purchased Brand Group, one of the country’s larger advertising and marketing services firms, and Applix, a fast growing digital scale-up.

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.