Banking Data Analysis

The bank of the future focuses on artificial intelligence

The pervasive and significant impact that digital technology is having on the banking sector is now undisputed. Like all sectors, financial institutions also need to reconcile two main needs – namely the maintenance of a highly competitive rate, including outside well-known geographical boundaries that relates with consumers who require ever simpler, yet effective, frictionless services; with an ever greater degree of customisation.

Artificial intelligence can undoubtedly serve the purpose, given its quiet but powerful impact, which enables the support and automation of internal processes, with the automated transactions  entrusted to the software, thus reducing errors, speeding up results and improving customer services.

How do banks offer change with the introduction of AI?

The banking processes that artificial intelligence can be applied to certainly include those that work on improving the user experience on all banking channels, but that is not all.

The main machine learning algorithms find application in all areas of forecasting and strategic business analysis, for which speed and accuracy of decision-making is fundamental. All of this is done primarilyusing a resource that is considered a source of profitability still unexplored by financial institutions, i.e.data.

Artificial intelligence to customise the offer and increase customer retention

AI can have a strong impact on the relationship between the bank and its current and potential customers. Interactions with all the bank’s touch points and cash points generate valuable data on potential and existing customers that can be collected to find out more about their level of satisfaction, behaviour or the risk of losing them; allowing the bank to then design an even more customised customer journey. An artificial intelligence tool can systematically help with the collection, analysis and classification of these data and return additional information that can feed the systems related to digital signage, file management at the counter, requests for appointments with specific consultants and much more.

All touch and cash points within the customer journey are aligned in showing marketing, product and service recommendation messages, producing an experience that involves and focuses on the customer. Furthermore, AI services can help develop personalised loyalty programs and devise new services that are well suited to specific types of users in order to promote customer retention.

Artificial intelligence for strategic and advanced data analysis

Given the amount of data that financial institutions have at their disposal, both in relation to business services and their customers, it has become essential to have AI tools available for the analysis of these data sets.

In this case, the result of operations based on data mining and machine learning algorithms is to identify correlations and  reusable patterns. One of the functionalities of the AI-based system is precisely to study all the data of bank transactions and flow movements on active retail channels, in all branches and on all touch / cash points to provide results on trends, seasonality and profitability of every single service to better identify and implement targeted market strategies.

An advanced data analysis, supported by AI and usable through native integration with business intelligence platforms, enables the bank to develop new performance models and roles in the value chain.

Artificial intelligence for predictive analysis and risk management

One of the most natural applications of AI technologies is predictive analysis, i.e. the process that uses historical data to provide forecasts of future scenarios under certain conditions. For banks, predictive analysis can be applied to various internal processes, including the management of the life cycle of physical devices and the cash management chain. The forecasts generated by specific artificial intelligence algorithms are fundamental in optimising the management of operational risks and ensuring ever greater levels of service and continuous accessibility for users.

In terms of the supply strategy, it is essential that the bank takes action to maintain control to drastically reduce costs, including those related to device maintenance, money handling, or  general  costs involved in planning; and methods too little aligned with the required rhythms of the market.

In the case of asset management, artificial intelligence identifies patterns and trends in order to apply a forecast maintenance algorithm capable of obtaining extremely precise scenarios of future actions on each individual asset and on the future operation of the device.

Whereas with the cash handling process the starting point is the data, from each cash point used to develop a predictive model capable of predicting withdrawals, deposits and recycles, which is then applied to improve the most appropriate cash supply process for each branch or individual machine.

AI – Benefits for banks

The application scenarios of AI technologies are varied and do not just include the introduction of specific machine learning or deep learning systems, platforms or algorithms. It is above all the inclusion and metabolisation of these new tools in the organisational dynamics and existing technological structure that make the difference.

The AI module, integrated with the proprietary systems, makes it possible to make the most of the data already available to the bank, to facilitate and speed up strategic decisions and improve investments.

By exploiting artificial intelligence, the bank will be able to:

  • Ensure an even more personalised approach to the customer that does not exclude the human touch
  • Preserve investments thanks to immediate integration with omnichannel technology
  • Benefit from a digital transformation that involves all bank stakeholders, from management to the customer
  • Generate more and more confidence in a brand that is constantly being renewed by focusing on a smart, albeit simple approach

Artificial Intelligence in WinWebServer

Find out more about WWS modules that implement AI algorithms:

WWS Customer Management

WWS Cash Management

WWS Business Analytics Management

WWS Asset Management

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