Smart Data Analytics and AI: Banking Becomes More Intelligent

2. October 2019 / in Knowledge

Big Data is becoming Bigger Data. In the next decade, the amount of data across the world is expected to double every two years. That’s a whole lot of new data! However, it is only the clean and intelligent analysis of that data that gives Big Data true relevance. Smart Data Analytics makes it possible: by structuring the data, correlations can be identified and potential exploited.

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Once transformed into Smart Data, Big Data can be used in many areas. Whether in payment transactions, lending decisions or capital formation: processed data helps better decisions to be made across the board. In the financial industry, Smart Data Analytics enables banks and FinTechs to better understand customer behavior and continuously improve their services.

Just a buzzword, or real added value? In this blog post, we explain what Big Data is all about and how it is transforming the business world.

Smart Data Analytics thus also creates the basis for new business models such as online loans with real-time credit checks. The analysis and categorisation of data required for this is based on self-learning algorithms. In many cases, the pace of processing and analysis would no longer be achieved by humans today. Machine-learning algorithms, on the other hand, can evaluate large amounts of data in a matter of seconds and automate manual processes - without even getting into a sweat. For this reason, Artificial Intelligence will increasingly complement human intelligence in the coming years. 

In this blog post, we explain why digitalisation and AI belong together, what is essential to bear in mind when using Smart Data Analytics and Artificial Intelligence, and how the technology is already being used in banking. By the way, we wrote this article ourselves. However, your morning sports news may have been created automatically based solely on data from the games!

Not Without Each Other: Digitalisation and Artificial Intelligence

Infographic Smart Data, AI & Digitalisation

Digital processes require new technologies and new technologies require digital processes. These two concepts must therefore also go hand in hand in the financial world. When implemented correctly, they provide the industry with answers to one fundamental question: How will banking be ready for the future?

The wishes and expectations of bank customers have changed considerably in recent years. An increasing number of people are turning to digital, automated solutions in both their private and professional lives. They expect nothing more and nothing less from their banking experience. Digital solutions are therefore of paramount importance if banks want to ensure a positive customer experience.

In search for an answer to the question of how companies can remain relevant for their customers, the banking industry in particular cannot avoid the topic of digitalisation. As the digital transformation progresses, business leaders should urgently explore the potential benefits of using new technologies. According to the 22nd PwC Global CEO Survey, digitalisation and Artificial Intelligence are already at the top of the priority list for banks. A recently published study by adesso also points to the growing awareness of the importance of Artificial Intelligence in the financial sector.

Digitalisation and Artificial Intelligence

Surveys show that the work of persuading people has already been done. Now is the time to implement digitalisation and everything related to it - from Smart Data Analytics to Artificial Intelligence.

Digitalisation and Artificial Intelligence: A match made in heaven. We explain why.

AI in Banking: A Goal-Oriented Approach Is Crucial

To date, analytical depth and experience are not yet sufficiently widespread in the financial sector. However, data analysis using increasingly powerful algorithms requires a well-founded understanding of the content of possible modes of action. Last but not least, the presentation of data and evaluations must be so user-friendly that users in other areas can also work with them intuitively. For this reason, a thorough understanding of the technology is absolutely essential.

FinTechs that specialise in the technological aspect of financial products are predestined to develop and implement new technologies. They adopt a clear and goal-oriented approach. What specific problem should AI solve? Where and how does the use of new technology help to improve a service? The more concrete the benefits and the deployment scenario, the higher the success rate.

Until now, Artificial Intelligence has often been portrayed as an opponent of human intelligence. In fact, companies are increasingly viewing it as an opportunity to focus on people. In banking, the existing use cases for AI emphasise customer-oriented and user-friendly solutions.

In any case, the financial world believes in growing customer confidence in the new technology. According to a survey, financial experts estimate that 76% of bank managers would recommend AI-based applications for investments to their customers. However, the enthusiasm of the financial industry is not quite met with the same levels of excitement from its customers. At just 30%, they are even more reserved when it comes to entrusting their investments to an AI system.

Where Do Artificial Intelligence and Smart Data Analytics Already Play a Role in Banking?

Artificial Intelligence is not only awakening new aspirations in the industry; it is also already changing the value chains of established financial institutions and bringing new players into the game. Using it opens the door to new business models. The following examples show to what extent AI goes beyond the limits of calculation and reaches into the realms of understanding. The new technology is now about much more than intelligent automation. It is about insights that FinTechs in particular gain from the analysis and intelligent handling of data.

Structured and intelligent: Self-learning algorithms enable categorised customer data

Whether for automated lending, fraud prevention, leasing or debt collection procedures: Our  analytics platform represents Smart Data Analytics in action. Using Artificial Intelligence, the platform analyses transaction data from online banking and provides you with a categorised overview of the account data. You can identify risk characteristics in the transaction data in order to determine creditworthiness and identify fraud risks at an early stage.

For example, a data analysis can be used to create an expenditure account for a customer in order to evaluate their solvency and minimise payment defaults. Self-learning algorithms extend with immediate, standardized reports for products such as the tenant check, credit check or account holder verification.

Real-time ameliorates the customer experience

With the rapid growth of mobile and web-based applications, users are accustomed to accessing all their applications anytime, anywhere. Customers today want to use banking services on all their devices. In addition, they not only expect access in general, but also an integrated and seamless user experience. It is only digital solutions that can meet these customer expectations.

Possible uses within the framework of AI-based services go one step further. With the customer’s consent, Smart Data Analytics can provide immediate insight into customer behaviour based on transactional analysis and real-time capabilities. This will reveal, for example, when the customer might be dissatisfied with a service.

When the knowledge is subsequently used to provide personalised recommendations or support, such insights really do give a valuable competitive advantage. This allows financial institutions to integrate innovative services tailored to their target audience and offer added value to their customers. Using a banking API, they can directly offer these to their customers in the usual environment.

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AI modernises the payment process

In the media, Artificial Intelligence is often still treated as a futuristic and distant reality. In payment transactions, however, the technology is becoming more and more important. The payment industry uses machine learning algorithms to evaluate large amounts of data, also within the framework of new business models, and to automate manual processes. AI can already be found in three typical challenges related to payments:

Payment yes, but make it discreet

No one finds paying the most beautiful part of the shopping experience. For this reason, it is in the interest of everyone involved to offer a pleasant experience without media disruptions, which will ultimately help to increase the conversion rate. The identification and verification of customers should never come to the fore. Artificial Intelligence supports Payment Initiation Service Providers (PISPs) in creating a customer-friendly, frictionless payment experience.

Payment when and how the customer wants

Whether voice search at home, on a smartwatch while jogging or in the car on the way home: payments can and will be integrated everywhere. Mobile and multi-channel payments are no longer a distant dream of the future.

For example, the Italian UniCredit company Buddybank has long been acting as a buddy assistant for everything to do with banking. In Germany, comdirect has also been offering voice banking since the summer of last year - powered by AI.

Payments in real time

Since the introduction of peer-to-peer transaction, the payment industry has required fully automated backend processes that enable direct payments. Instant payments raise the bar even higher. They require extremely fast logistics and processes that are executed in real time.

Smart, swift, secure: Credit checks using the Digital Account Check

Granting loans is a prime example of how the banking sector can reap the benefits of AI-based technology. Before using Smart Data Analytics, both lenders and interested parties had to exercise a lot of patience. Today, in order to shorten this lengthy process, Big Data is transformed into Smart Data for the Digital Account Check

The first step is to collect all the important data, which is crucial for the Digital Account Check. Big Data serves as a starting point to extract and categorise relevant information. This includes, amongst other things, the transaction history.

The second step is to evaluate the useful data. On the one hand, this is to prepare the expenditure account and to calculate the debt service capacity. On the other hand, it is to filter the volume of data based on criteria relevant to creditworthiness. This way, the lending bank receives exactly the information it needs - aggregated and individually categorised for it.

3. Smart Data für den digitalen Kontocheck

More examples of the benefits of Smart Data Analytics? Read here how house banks already use intelligent data analysis to their advantage.

Artificial Intelligence and Smart Data Analytics - The Right Way

Digitalisation, Data Analytics and Artificial Intelligence are closely linked and benefit from each other. They all live and breathe data. Digitalisation not only works with data every day, it is also one of its sources. Computers collect data, and, thanks to machine learning, learn how to perform tasks more effectively.

Just as a child learns from impressions, AI technologies also need impressions, which they receive in the form of data and information. Smart Data Analytics also only works if there is a sufficient supply of Big Data. Consequently, the procurement and intelligent processing of data are decisive for using the technology - and thus for the future of banking.

One thing is clear: Data and the processing of it using Artificial Intelligence remains an issue and will become even more important in the future. After all, there is no halting technological progress. As mentioned at the beginning, the volume of data will double every two years in the next decade. An estimated 175 zettabytes is already forecast for 2025.

Business leaders in the financial sector should not hesitate to analyse how they can best use this wealth of data. Even Bill Gates would agree with us: “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”

Is Smart Data Analytics also a big topic in your organisation? Find out more in our white paper on Smart Data Analytics!

Schlagworte: Knowledge

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