Over a year ago Gartner warned that organizations with actual maturity in BI and analytics are quite scarce. Data from other sources are also far from optimistic: Big Data, AI and advanced analytics projects flounder because organizations are unable to determine precisely what role the introduced technology is to play for end users, or for the business itself.

Perhaps the root of the matter is not the technologies themselves, but the appropriate skill set of the project team? The knowledge and experience of individual team members need to complement each other, especially in IT projects. Choosing the right skills and the right proportions is crucial.

Data Science: foundation or fad?

The Data Science sector is booming, and Data Scientists are in demand on the labor market. So much so, that they were dubbed “the unicorns of the labor market”. Due to their unique combination of skills, these new-generation specialists support AI, ML or NLP teams. In one Scandinavian bank, the Chief Data Officer is building a team of 100 Data Scientists, supported by 50 Data Students. The sheer scale of Data-Driven Product operations in banking indicates that the area will grow, challenging companies such as Amazon in exploiting the newly discovered data potential.

However, there are some critical voices given the massive demand for Data Scientists, coming not only from HR departments. It seems that the demand for these highly-specialized team members is only partially justified, stemming from a kind of fad caused by inaccurate understanding of the demand for specific skills in the transformation project team.

For example, a Data Scientist is not a synonym for Analyst; his task is to delve deep into the analyzed data. However, he often ends up with a job description of Analyst, Statistician or Data Engineer. Each of these roles is associated with the need for a slightly different set of skills. Their inadequate selection for the project may end up with frustration for both sides, the company and the employee himself, as the various roles required for IT projects have very different job descriptions:

> Data Scientist

With the combined skills in mathematics, statistics and computer science, Data Scientist adds value to data, to create new services or optimize existing ones. Due to the demand, it is the highest-paid role in the team.

> Chief Data Officer

CDO oversees Data Scientists and Data Miners, and is responsible for the company’s information resources and adequate management. He liaises between the IT department and business but also ensures the proper exchange of data between clients, partners and suppliers.

> Statistician

Statistician delivers well where traditional analytical techniques are involved, e.g. regression models and significance testing. The role is especially appreciated in the business context, e.g. improved marketing targeting or business analytics. In banking IT, statisticians with previous experience in economic analysis departments are a genuine asset.

> Data Engineer

Usually focused on back-end solutions, he designs how the architecture of all relationships between different data sources come together to tell one story. He deals with data modelling (ERD), ETL structures and frameworks, and integration of many data sources into one model.

> Data Miner

His task is to search for information in various company data sources, as well as to sort and qualify them for use by Data Scientists. Data storage and management structures, as well as platforms related to large data, e.g. Hadoop, are their daily bread.

> Cybersecurity Specialist

Defines technical and organizational measures to combat more and more sophisticated threats, even before the data is processed. The area of cybersecurity includes many specializations: technical, auditing and testing. 

Roles and functions as a step to BI maturity

Data-Driven Banking model allows offering complementing services based on the “by-products” of these organizations, i.e. data. It is also an opportunity to build the image of the organization as an employer: innovative projects attract the greatest talents in the field of Data Science.

However, when dealing with such a demanding area as Data-Driven Banking, the right balance of skills and experience of team members vs role demand is very important. As a software manufacturer, in our proprietary project Payres, we are not excluded from the considerations of the optimal combination of roles in the Payres project team.

What is your experience with Data Scientists? Do you think the high demand for this role is justified? Looking forward to learning your thoughts on this, feel free to drop me a comment.

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Sounds insignificant to you? Nothing could be further from the truth. Younger generations pay attention to these matters much more than my peers.

Shift change

Banks can see a new generation change very clearly. Family funds, and sometimes significant fortunes, begin to flow from baby boomers to millennials and their successors, Generation Z, whose values and tastes are radically different from the ideals of their predecessors. Therefore, it is evident that the existing methods of building customer loyalty will soon prove ineffective.

In the UK, banks are already paying cash to their customers for transferring accounts from the competitors. This shows that banks are walking a tightrope in terms of trust, as modern technology companies lure their customers away. So it is obvious that they must adapt to millennials by making the offer more attractive, and changing their communication style. During the summit, Bartłomiej Dajer from Credit Agricole asked the perverse question whether customer loyalty in banking is even possible at all.

Monzo vs Revolut 

The session that especially caught my attention was Monzo’s. This fintech startup, or rather: a neobank decided to buy a banking license, unlike Revolut. Both companies strive to meet the regulatory requirements, however, Monzo’s ambition is to be perceived as a bank. At the same time, Revolut has no intention to invest in the license, even though it publicly claims full compliance with strict banking regulations regarding customers. And as a matter of fact, the customers have been trusting them.

Monzo and Revolut represent two different approaches, two different business models. Both ‘banks’ are growing very quickly, but it is Revolut that has broken the barrier of 10 million users.

Revolut’s innovative product, a secure card with extremely favourable exchange rates, can convince even the most distrustful customers. Revolut clients are travellers, at least most of them. So, what will happen to Revolut during the coronavirus pandemic? Will people still use their card back in their country? Even if they will, we need to remember that the Revolut business model is based on currency exchange margins, which are calculated upon the client’s travels. The unusual situation we are facing now will undoubtedly affect the company’s revenues. Of course, Revolut is trying to expand in other directions, but will they make it on time?

Speed up the changes

One of the summit’s critical topics was the problem of quick response to changes, or rather the lack of it. Some banks have already noted that they need to focus on this area. Our partner, ING Bank, has abandoned the long lasting RFP process or the waterfall approach. Instead, they switched to the iterative and incremental approach.

An interesting approach was presented by Backbase. Their operating model, developed in the course of projects for Societe Generale or BNP Paribas, cuts the decision making process regarding a project to 6 months. The goal is not to exceed even a year’s quarter. As emphasized by Backbase’s speaker, for banks that use the standard approach, the timeline is approx. 2-3 years! Instead of querying several bidders, Backbase experts recommend scanning the market, setting evaluation criteria and developing a Proof of Concept as soon as possible. Besides, they advise to send RFI not only to companies that first come to mind, but to expand the search systematically. The best bidders in a given industry can be found in the databases of Forrester, Celent, Gartner or IDC. According to Backbase, it is worth talking primarily with industry specialists or at least starting the search with them.

People, not clients

For a very long time, the client-centric paradigm was the primary approach in banking. Everything would start with the customer as all the products were developed for them. During the Digital Finance Summit, it was officially announced that everything starts not with a customer, but with a person.

Banks are entering the role of life advisers, working not through the perspective of products they want to sell, but through the people’s needs. In the new paradigm, the banks are not focused on offering only financial products, but also on supporting people in various life situations. Of course, this wouldn’t work without the numbers (revenue metrics?), and specific products, the range of which expands. 

Naturally, banking rhetoric is changing and the industry is walking out of Excel, launching on a journey to communicate with people. It is not about calling the customers to offer them a loan anymore, but about talking to people, understanding their situation and trying to provide support to meet their ongoing needs, without focusing on one product. 

This change of tune will no doubt trigger a strong emphasis on personalization. Reaching an individual cannot be based on random trials anymore. Effective operation in a new paradigm includes collecting enough information to know exactly if the bank’s offer matches the needs of the individual. However, personalization requires activating data. In the age of constant changes, only real-life data can be a source of information that would allow reaching people effectively.

CCA Europe has been working on proprietary IT tools for activating financial data for several years. For more information, visit our website, or contact us on the private channel – we are here to help you.

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The banking sector is increasingly adopting Big Data and Cloud into their business models. Deloitte reports that just 25% of European banks feel confident and are ready to implement the changes. Banks are teaming up with small FinTech companies and start-ups or big players as Microsoft to develop better tech abilities and deliver their products in the best possible way.

Netflix-like Bank

As with any other service these days, customers expect tailored services from their banks. Accenture’s 2019 Global Financial Services Consumer Study states that one in two customers would like to get reports on spending habits as well as advice on money management. Trying to meet these needs and expectations, over 40% of finance-related companies consider adding personalized services to their offer, according to Econsultancy. 

Unfortunately, as revealed by TheFinancialBrand, 94% of the banks do not deliver proper personalization for their clients. However, there are still a few examples of good practices. 

For example, Bank of Ireland started to use customer data in a way that is similar to organisations like Netflix. Using online and offline data, the bank advises customers with the right action at the very moment they need it. 

The personalized content in customer communication has increased the number of loan application submissions by 15%

Another bank following the Netflix path is Aion, the new arrival on the Belgian market, launched by Wojciech Sobieraj. Based on a subscription model, Aion gives access to the services by monthly subscription. Customers can purchase one of two different options; basic services or advanced plan, including the tool MoneyMax, which helps to improve money management.  

Italian automation 

As reported by McKinsey, automation can cover even 30% of the workload in banks. Apart from minimizing costs and increasing productivity, automated workflows allow banks to re-organize their personnel and systems. 

A great example of workflow automation implementation is Italian bank Banca Popolare di Sondrio. Moved by a need to quickly launch new customer services and keep control costs under, Banca Popolare di Sondrio invested in automation. Thanks to that, the organization managed to decrease the average transaction handle time from 1 hour to 10 minutes. With flawless processes in place, the quality of customer service took off, followed by time and cost savings. Within the three coming years, Banca Popolare di Sondrio expects cutting costs in 40% of development and testing phases through intensified productivity and increased efficiency.

When partnership means security 

Apart from money, banks handle a massive amount of data, which makes them, even more, a target of potential attacks. To protect both the clients as well as the organizations, investment in cybersecurity is crucial. Fortunly forecasts global spending in this area to surpass $6 trillion by 2021. CitiBank has already invested a lot in Big Data technologies and startups. Their partnership initiative with tech companies, Citi Ventures, is a good example of how a bank can focus on cybersecurity. The recent investment, a data science company Feedzai, helped CitiBank to minimize financial risk for online banking providers. Thanks to the real-time machine learning and predictive modelling solutions, CitiBank can now track possible frauds, unusual charges or any suspicious transactions. Their clients are notified about the detected anomalies on their accounts. At the same time, Feedzai helps providers and retailers to monitor all financial activity and identify threats related to their business.

The wind of change

Focus on customer experience, automation and higher investments in cybersecurity will inevitably impact the banks’ offer. With many new fin-techs emerging lately as an alternative to traditional banking, banks need to reestablish their operations, processes and adopt new approaches if they want to stay competitive on the market. 

Changes should take place quickly, and the industry is starting to realize the fact. At the latest Digital Finance Summit conference (a more detailed report coming soon) Pierre-Alexandre Boulay, Head of Sales CEE from Backbase postulated moving away from the long-term RFP (Request for Proposal) procedure. Instead of a long-lasting process, sometimes taking up to 2 years, he proposed a more agile approach: using a Proof of Concept and a quick decision-making framework to start projects within 6 months. Will this be a game changer in a way to compete with aggressive fintech strategies? In my view it could be one to put the bets on.

Still, the potential of data-driven banking has not yet been fully discovered and exploited. Only a small number of banks have been successful in data-leveraging as 85 % initiatives normally fail in this area. Learn how CCA Europe’s solution could help you use some of your data to your benefit. Contact us for a discovery call.

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Technology spendings in banking are going to increase by 4.6% in the coming year, according to Celent, bringing further changes in the offered services and technologies used. In 2020 banks will focus on the clients, even more, meeting their needs by improving customer service, security, and using data to become faster and more reliable. What does it mean for us, clients? 

Customer-centric perspective

Seamless customer experience and consistency across different channels are the goals for all the industries. The same goes for banking. The sector has been investing heavily in technologies and new business models to meet the client’s needs and expectations, e.g. mobile app ATM withdrawals — the process of introducing near-field communications technologies to the ATMs. Adding services like Apple Pay or Google Pay is just the beginning. Soon, we will experience wide adoption of the QR codes in instant payments over social media. 

Banks will also focus on personalized, real-time customer service by using smart bots. According to Gartner, chatbots will handle more than 85% of customer service interactions in 2020. The bots will be not only about simple queries – they are going to offer more advanced financial tips, detect fraudulent activities, and even assist customers during the registration process. This technology not only helps the banking world to provide centralized financial management but also improves the way customers connect with their banks. 

Bitcoin and biometrics for improved security

Since banking has always been one of the most attractive targets for cybercriminals, security solutions need always to be one step ahead of them. Soon banks will use more and more cryptocurrency payments to provide safer, cheaper and faster transfers. Some are already creating blockchain systems with digital currencies for their customers, e.g. Bitcoin, which is a secure and verifiable way to keep track of transactions. The payments and currency exchange are transparent, and therefore, they are very advantageous to be used in banking. Customer data is protected in the decentralized database, in which all the information about real-time payments and profile details on multiple servers are stored. 

In 2020 we will also see more sophisticated biometric authentication into mobile devices. Fingerprint identification or facial recognition will surely be considered as a special precaution in many bank apps, until the day they replace passwords altogether. 

As an additional layer of customer authentication, banks are also going to introduce voice commands. This feature will also be used to make inquiries about transactions, transfer money or report stolen cards.  

Data upshifts business

Shortly we can expect an increased focus on Big Data, not only to identify security threats in mobile and online banking. The value of the big data market in 2019 reached USD 29.87 billion, and by 2025 it is expected to double. Since data-driven organizations are 23 times more likelyto acquire customers than their competition, no business can neglect the importance of data, especially not the banking sector. Banks will take advantage of data by  leveraging them to understand the needs and preferences of their clients, thus creating real value of new experiences, faster business decisions or real-time services. Based on data, banks will help their clients manage their spending habits and control finances. More data is also going to influence the appearance of digital-friendly products thanks to collaborations with third-party providers, such as fintech and startup companies.

Data analytics will also fuel growth and new business models, in which banks will be able to reap income thanks to data, for example, by sharing customer-analytics capabilities with new ecosystem partners. Great analytics will have to go hand in hand with building trust and being relevant to the clients. 

Banks of the future. What to expect?

Technological development in the last decade changed the way we consume and how we operate with our money. In ten years cards and apps might be replaced by more advanced payment methods, e.g. using biometrics, to make the client’s experience more personalized and satisfying. The era when banks were all things to all customers is long gone; the data-driven ecosystem will rule the financial future. Relying on valuable information will be the starting point to enable the process of disruption in banking.

I would love to hear your thoughts on the future of banking technologies. Please drop me a comment! And if you need more information on how to leverage data in banking, feel free to contact me.

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Traditional banking has to face either fear or some anxiety. Stable, secure, but overwhelmed with their weight and regulatory requirements, banks are not able to keep pace with agile fintech companies, and many times must step aside. Which bank would be able to rewrite its entire payment processing platform three times in five years – exactly like it did AliPayments, achieving 1 million transactions per second? Maybe, instead of racing with fintech, banks should do something else.

2 main trends in banking

During the last Fintech Connect London conference (3-4 December 2019), I observed two main development directions in banking. The first one is B2B payments, the second – activation of collected data in a way they can start working for the bank. Banks in the UK consider themselves to be the most advanced in Europe, but they are still just scratching the surface of data science and B2B payments. Even though large entities create departments of innovation and cooperation with fintech companies, there is no security that popular now, financial start-ups will want to cooperate with them.

Although B2C payments are already mostly available in real-time, regardless of the location of the recipient, and have quite reasonable margins, settlements between companies or institutions are still a long process. In my opinion, this is a new line of cooperation, and at the same time rivalry between banks and fintech companies: who is going to be first and who will do it better. We will observe the development of the situation because as a software house specialized in banking solutions, we are familiar with payments, and we can support them, in terms of new ideas or experience.

One hundred data scientists in one team!

London gave me an exciting inspiration related to the opportunities given to the banks by the massive amount of data they administer. One of my interlocutors, Chief Data Officer in a Scandinavian bank, talked about the Data Scientists team of 100 employees and a Data Students team, consisting of 50 people who are not yet formal employees. The scale of their activity indicates that this area is going to develop further to catch up with companies such as Amazon in using the newly discovered potential. This so-called terra incognita consists of Data-Driven Products, which banks will be able to offer next to primary, mass-supported financial products, such as cards and bills.

Undoubtedly, banks will not be able to share all the data, and none of us would want it. Still, with proper aggregation and anonymization of data, we can already think about how to monetize specific statistics, in terms of segmentation of a given market or even individual corporations. For example, a chain of stores can collect and use data on purchases of their customers, but it is much harder to find industry statistics or a specific customer segment. Banks could process and share such data.

Market data in real-time

Soon, banks will start providing reports on specific market categories, which perfectly fits in with our Payres product, a solution for monitoring, aggregating and announcing payment and card transactions in real-time. In London, I learned that some banks have also created their Payres-like applications, but they are operating with some delay.  Our solution can provide data to a specific customer in real-time so that individual trade actions are tracked. If, for example, Black Friday in all branches sales go high, and in one branch it is really low, Payres will show it right away – not in a week or two, but in real-time, enabling immediate response.

For some time, banks have been looking for a product that they could offer in addition to financial services. Some choose insurance, real estate, and others sell airline tickets with cards. I wonder how many banks are already focusing on translating their data into a specific product – and for whom? This type of product must be dedicated to a corporate audience, not a retail one. Why? Corporations will gladly pay for such a solution because it will provide them with an additional dimension of data, especially if they can receive it regularly. Multinational companies will probably be interested in buying data for a specific market to make decisions about development, acquisition or merger, and enterprises would choose a data subscription to get instant information about their market shares.

Bank as a research institution? Will real-time market data be available in the SaaS model? Banks have a future in offering complementary products that are ‘by-products’ of their work. Do data-based products have a chance to fight for supremacy in this field? Will banks take advantage of the opportunity offered by their resources? I’m curious about that.

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