A new competitive landscape

There is no doubt that Artificial Intelligence (AI) is shaping the financial industry, from new entrants to traditional players. Financial Technology (FinTech) start-ups already leverage the latest AI advancements to offer customers cost-efficient solutions. Robo-advisors, for instance, provide algorithm-driven financial services at a very competitive price. A new wave of digital banks is appearing, enhancing the customer experience with AI-enabling tools and processes, from onboarding to support. Last, more and more banks or funds use AI to automate their processes and trading decisions, and improve their profitability.

Various use cases, from back to front-office activities

In this context, financial institutions are wondering if it’s the right time to hop on the AI journey, or how they would best benefit from it, while others are heavily investing in AI, staying ahead of the curve. To inspire our undecided clients, we have identified three relevant areas of applications:

  • Back office automation: Human judgment stays the main driver for decision making in the financial industry. Thus, for back office, AI finds more value in process automation and is leveraged to support employees in recurrent and time intensive tasks. In this case, AI is not directly applied to finance, but the applications are diverse such as the interpretation of legal contracts or the automatic classification of emails and documents.

  • Customer service: Customer service is expected to be the most disrupted by AI on the short term. Especially in banking, AI could be the key to more personalized services and to information consolidation. Leading banks already introduced chatbots as a complementary communication channel with their clients, offering from simple operations (account management, payments etc.) to more advanced ones as instant credit worthiness evaluation and custom advice about the bank’s services.

  • Fraud detection: Financial institutions are giving importance to fraud detection. For this purpose, behavioral algorithms are improving existent rule-based Anti-Money Laundering systems (AML), by prioritizing the list of detected frauds and reducing the number of falsely detected scenarios. Another alternative for fraud detection is the monitoring of employees’ work activities to automatically identify rogue behaviors.

Challenges to tackle

While the technology is becoming mature, and the incentives for AI are clear, there are still challenges to be considered, as for any disruptive innovation:

  • Technology and resources: Usually, financial institutions do not possess the necessary know-how and the technology readiness for AI projects. They also lack profiles such as data scientists required for the development of AI solutions. The maturity of the technology and the variable relevance to their use cases are also common inhibitors.

  • Privacy of customer data: AI algorithms in finance rest on highly sensitive data. Financial institutions must adopt secure cloud solutions in the right regulatory environment to guarantee privacy and security.

  • Regulatory Oversight : The financial industry must bridge the gap between fast evolving AI applications and underlying policy and laws. A compelling example is the need of regulations for algorithmic trading, to name just one.

A simple way to go there

OpenWT’s experience shows that 3 steps can help answering the complexity of AI for financial services. Recently, we applied this methodology to a leading financial institution to automate back office activities.

  • We conducted an ideation workshop to identify AI-related opportunities, in this case: classification and routing of internal emails;

  • We designed and prototyped a solution integrated to their environment, using real data (Proof-of-Concept). To do so, we used state-of-the-art AI enablers built by the research teams of our partner Swisscom;

  • As the Client was convinced by the AI-enabled classification, we scaled the prototype up to a production grade system;

This solution brought our Client tangible results: decreased processing time of tickets, reduced operational costs and downtimes, and increased productivity.


Ready to go for AI? Our teams are here to support you in this journey. Do not hesitate to contact OpenWT’s AI specialists to start the conversation.

Need more inspiration? Discover companies that already started their AI journey.

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