Redefining banking using advanced analytics


Artificial intelligence (AI) represents one of the biggest disruptions the business and financial services world has seen. The technology which helps to augment human intelligence has arrived across all industries, and the financial services industry is no exception. Although using smart algorithms is not new to the sector – for example, it is established in credit scoring or data-driven decision-making in trading firms – the recent explosion of available data across various channels has fundamentally changed the way wealth management and banking will be implemented in the future.

Market needs

New technologies such as predictive and advanced analytics, machine learning and AI are already influencing almost every part of the financial sector. Wealth managers will need to follow – and capitalize on – current trends and move towards personalization by offering clients a service that is specifically tailored to their preferences.

Advanced analytics is key for providing smart, personalized products and services. But AI is not only about increasing revenue; it also helps to achieve back-office efficiency. These new technologies are increasing relationship manager (RM) effectiveness, using targeted pricing and strengthening operational efficiency.

The use of advanced analytics techniques has already enabled a more client-centric approach for fintechs, which have managed to design versatile business models with a real, data-centric focus to target the profitable business lines of traditional banking.

To compete with new market entrants, banks will be required to enhance their analytics capabilities and set up the necessary infrastructure to process all their data. Traditionally, this data is stored in siloed systems and used within specific business sectors and jurisdictions. But, while obtaining a holistic view of their clients and developing the ability to integrate and combine many types of data are essential, realizing it from siloed systems poses a significant challenge. When done properly, however, data integration can lead to significantly increased efficiency, business growth and cost and risk reduction.

A healthy client relationship, which is enabled and enhanced via digital services and experiences, is key for every bank to differentiate itself from its competitors. Advanced analytics will help RMs to better engage with clients with smart alerts and individual product recommendations. These services will follow a human-centric design and will be fully embedded and designed together with new engagement and wealth management platforms.

Avaloq vision

Useful insights

Big data technologies have introduced automation, which expands the pool of people who can benefit from data analytics insights (data democratization). Data science models provide a holistic view of available data and extract meaningful and actionable insights that enable subject matter experts to make better decisions. Our vision is to put data, in the form of useful insights, back in the hands of the decision makers while ensuring that they do not need to have in-depth knowledge of coding or big data technologies.

Data science solutions

Closely in line with the above, these new technologies should support the user in delivering the highest-quality services by lessening the administrative burden and assisting them in smartly prioritizing their work. AI and advanced analytics should provide a positive impact for the business, influencing how the business interacts with clients and should not just be used as a marketing instrument. That is why Avaloq has developed data science solutions with a strong focus on human-centric design.

Predictive power

Machine learning technologies, such as deep learning, require lots of training data to perform well. As a core banking provider with a wide client base, we are able to provide our clients with industry-leading data science solutions with unrivalled predictive power which will significantly increase the efficiency of our advanced analytics solutions. A relatively new concept called distributed (federated) learning, which has also been applied by large technology companies such as Google, allows us to improve our algorithms without compromising on data privacy.

All the AI solutions we develop observe the highest ethical standards and follow the Ethics Guidelines for Trustworthy AI published by the European Commission.

AI Guidelines


The next six months

With our team of data analytics experts, Avaloq has built a state-of-the-art insights platform, tailor-made for use in the banking and wealth management sector. We combine the strengths of our unified semantic data model, which guarantees unmatched data consistency and integrity, supporting all your clients from retail savers to ultra-high net worth individuals. The platform helps our clients to immediately extract value from their data, without dealing with all the complexity of building up a new data science and analytics platform.

Together with some of the industry-leading network and graph analytics and natural language processing (NLP) experts, we’re developing a ground-breaking way of gathering and visualizing data about existing and future clients. This will not only help to increase the client base but also to better serve existing clients and proactively react in terms of at-risk relationships.

In collaboration with one of the leading portfolio risk providers, we’ve developed an innovative approach to continuously monitoring a client’s portfolio, including suitability and PRC/PRG checks, instrument and market monitoring, and stress testing and simulations.

The next 12-24 months

Using our AI-based portfolio rebalancer, it will soon be possible to construct – without any shortcuts – a client portfolio which takes into account the financial goals of the client while simultaneously being in line with the banks’ financial market view. Together with a newly developed portfolio strategy monitoring concept, an NLP-driven investment proposal and a storytelling solution, our wealth management platform will soon be supported by some of the latest AI innovations in the financial services industry.