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From Telephone Trading to Artificial Intelligence

valantic and Artificial Intelligence

Journey into the Future

Within the last decades, every industry branch passes a period of transformation – and banks are no exception. Long-established systems like floor and telephone trading have already been replaced by electronic solutions. Progressive digitalisation also has an impact on the general business model and, at the same time, offers new opportunities. With each response, each transaction and each deal a large number of data is accumulated and consequently, valuable conclusions can be drawn: Artificial Intelligence is the magic word which, also for banks and the finance industry in general, opens doors to wide-ranging possibilities.

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Table of contents

Artificial Intelligence Helps to Secure Competitive Advantages

Prospectively, Artificial Intelligence may help banks to make data processes more cost-effective and more efficient and thus reach decisive competitive edge. On the downside, banks have to touch one of the most important and trustworthy values: the own transaction data. The use of Artificial Intelligence is still in its infancy in German banks; institutions realise very slowly which benefits AI may generate with regards to the competitive situation.

Graphical representation of the application areas of AI solutions such as machine learning, compliance, risk data, trade, payment data, customer data and fraud detection

As long-standing software provider, valantic also works on solutions for utilising Artificial Intelligence in banks and financial institutions. AI will not be able to replace humans but can, in future and in parts already now, do the groundwork for the final decisions made by humans.

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White Paper: Artificial Intelligence

Download our white paper and learn more on how innovative systems and AI may help to make data processes more cost-effective and more efficient.

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Benefits and Fields of Application

Fields of Application

Categories of Machine Learning

The field of Machine Learning is divided in several categories or technological approaches: Up to now, the most common variant is Supervised Learning whereby the human is necessary to teach the algorithm the correct conclusions. The most common use case for Unsupervised Learning is the so-called clustering. It is needed for creating and forming groupings in order to visualise large datasets and corresponds to a rather statistically oriented model. Learn more in our white paper.

Applications for Artificial Intelligence in the Finance Industry

Science and AI at the Service of Financial Economy

Not only companies but also science is dealing with AI in the financial industry. In a comprehensive report, the institute ibi research of the University of Regensburg examined modern fields of application for Artificial Intelligence and predicts the future importance for the financial sector. The results of the report “Einsatzfelder künstlicher Intelligenz in der Finanzdienstleistung“ (Artificial Intelligence in financial services), published in July 2019, are quite interesting: With respect to the frequency of AI in everyday work, 51 per cent of the interviewed persons report to deal with AI on a weekly, 33 per cent on a monthly, 10 per cent on a yearly basis and 2 per cent never deal with AI-specific issues.

Risk Assessment for Consumer Credits or Fraud Detection

Cybercrime is a growing global problem that will have caused a damage of prospectively up to six trillion US dollars by 2021. According to the Federal Criminal Police Office, reported cybercrime alone in Germany has almost doubled in 2016 – the estimated number of unknown cases being much higher. The increasing level of digitalisation and the relating automation of financial services create new potential for fraud but, at the same time, offer chances for the detection of criminal patterns by means of automated mechanisms.

Optimising Customer Support and Sales

For customer support and sales, experts also estimate a significantly increased importance of AI. According to experts, the pre-sorting and categorisation of customer e-mails will be autonomously performed by AI solutions within the next ten years. However, the trend towards B2C solutions is obvious, particularly as, within this interview, this section of banking business was paramount with the optimisation of the customer experience.

Providing Indicators for Traders in Electronic Trading

In electronic trading, processes like the price calculation are affected; algorithms are able to learn and quantify, and to give the trader useful indications. This means, the machine learning algorithm learns behavioural patterns from recent requests and business data in order to forecast whether the proposed price would encourage the realisation of a business in a certain situation. An automated review and estimation of the margin between bid and offer price would significantly contribute to shorten the process. Thus, the trader himself/herself can focus on strategic trading decisions, is still the controlling entity and makes the final decision.

valantic and Artificial Intelligence

Quick Wins

Artificial Intelligence Helps to Secure Competitive Advantages

Prospectively, Artificial Intelligence may help banks to make data processes more cost-effective and more efficient and thus reach decisive competitive edge.

Benefits and Fields of Application

With the usage of AI systems, banks and financial service providers see more velocity, more efficiency and higher earnings in the ongoing business operation. The application possibilities are not only limited to the transaction side – quite the contrary, in the first place this technique is used in consumer transaction.

Typical Applications for Artificial Intelligence in the Finance Industry

On the basis of aggregated trading data, models can be created being the groundwork on which the software makes automated decisions. First banks already use Artificial Intelligence to improve the risk and compliance management. valantic is also working on projects to draw knowledge from completed transactions and create even more added value for banks.

Ways to a Successful Usage of Artificial Intelligence

For a successful usage of Artificial Intelligence, different challenges such as non-transparent processes, time-consuming data enrichment and preparation, data security concerns as well as insufficient system integration need to be addressed. In all points, valantic is at your service as strong partner.

Intersection between Humans and Machines

Conversational AI can network devices and applications through natural language to control operational processes.

Your Contact

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Andreas Schebesch

Division Manager Customizing
valantic