No one believes that banks will be directed by robots or that customers will be served by androids. However, for over a decade now, the financial industry has been striving to be at the cutting-edge of this technology and integrate the latest advances into their business.
What will the financial sector look like in the future? Will we be surrounded by human-like robots who help us just like office personnel do now? This question is becoming increasingly common in our society. Although we don’t have the answer, in this article we will analyze how artificial intelligence will help the financial sector become more profitable and make life easier for customers, optimizing their financial health and therefore, improving their lives.
In all sectors, and especially in banking, the main goal of artificial intelligence is to boost the productivity and efficiency of internal processes. This means cutting costs and offering customers private, personalized banking, with a better user experience that results in their satisfaction and loyalty.
Smart conversational banking
The current trend in the financial sector entails the migration of the traditional digital banking we have been using in recent years to smart conversational banking that offers completely personalized private customer service.
This is only possible through the development of a software based on the synergy between artificial intelligence and data. Artificial intelligence provides an extensive set of algorithms for various tasks that only work if they are supplied with previous experiences from the customer’s activity – in other words, data.
This synergy will generate customized, personalized and private customer service. And not just that, this software will also be capable of communicating with customers using their own language, giving rise to smart conversational banking.
Let’s take an example from this smart conversational bank. Imagine that a customer named Juan “likes” a picture of car he wants and writes the following comment, “I would love to drive it.” His bank notices this and makes him an offer in a private chat window about how he could adjust his finances to be able to buy the vehicle he wants: “Hi Juan! If you want to drive that car you should reduce your water and electricity use by 15 percent, pay for your vacation in installments and sell your current car for around €15,0000. Would you like to talk about it in this private chat?”
This is how a financial advisor would act, looking out for your household economy whenever you need it, 24 hours a day, no matter where you are, speaking to you in the language that you understand – all just a click away.
The current trend in the financial sector is to migrate traditional digital banking to smart conversational banking that will allow customers to consume services through interactions in natural language, how they are accustomed to communicating and feel most comfortable. Using their own voice or text through any device (cell phone, tablet, smart watch, speaker or television), the customer can request information, purchase products or perform bank transactions.
Furthermore, smart conversational banking is no longer static on the bank’s website or app but integrated in the communication channels customers use the most. From WhatsApp, Facebook or Instagram to virtual assistants like Google Assistant, Amazon Alexa or Siri, customers can talk to their bank to do anything they need to manage their financial products. Users are perfectly familiar with these apps, which they use on a daily basis, and their bank is integrated in them, becoming just another contact they can talk to in natural language to manage their financial activity.
Data, private banking and personalization
On the other hand, services are also changing. They are no longer limited to the traditional services offered by today’s digital banking. Instead, the anticipated private, personalized banking services improve customers’ financial health in a totally ergonomic manner, adapted to their specific needs at each point in time.
In order to obtain this personalization, the key lies in data. Each customer’s daily financial activity generates a historic volume of data and an experience that is impossible for human minds to process in an agile manner. Thanks to artificial intelligence algorithms, however, it is possible to detect and learn from behavioral patterns.
A financial institution has a very large amount of data on customers. It knows when, where and what they buy; their household expenses, where they work, their entertainment preferences and how much they earn. If data from outside the financial system are also added, knowledge of customers would be even greater. The more data given to artificial intelligence algorithms (variety and quantity), the more precise these behavioral patterns are.
With each new move generated by the customer, the artificial intelligence algorithms refine their learning. Each new input makes customers’ behavioral patterns more precise. This makes it possible to offer customers predictive and prescriptive services that improve their financial health: anticipating what expenses they will have and recommending them the best product based on their financial activity at the time.
- At home, a customer can ask his or her smart speaker, “Can you help me save?” Integrated conversational banking will answer by saying that studies of anonymous people with similar profiles show that they use 24 percent less water and 19 percent less electricity. These services can help change customers’ behavior at home in order to save money and improve their financial health.
- Via the WhatsApp app on a customer’s cell phone, he or she can ask conversational banking, “How much will I spend on restaurants during vacation?” “I estimate that you will spend €160 at restaurants and €28 at cafeterias,” will be the response.
- When making an online purchase, conversational banking can temporarily pause the purchase, telling the customer “I don’t recommend that you use your debit card to pay because your account will be overdrawn next week. You should use the credit card.”
The future of artificial intelligence in the financial sector will, without a doubt, make life easier for customers, developing systems that are capable of automatically understanding the situation and context based on data taken from new sensors and devices that allow for better decision-making in dynamic conditions. These algorithms may also try to decipher customers’ emotions and feelings in order to have interactions and conversations that are increasingly similar to humans.
- Banking Comprehensive Risk Management System Based on Big Data Architecture of Hybrid Processing Engines and Databases. Shenglan Ma, Hao Wang, Botong Xu, Hong Xiao, Fangkai Xie, Hong-Ning Dai, Ran Tao, Ruihua Yi, Tongsen Wang. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2018: 1844-1851
- Cognitive interaction with virtual assistants: From philosophical foundations to illustrative examples in aeronautics. Denys Bernard, Alexandre Arnold. Computers in Industry 107: 33-49 (2019)
- Conversational Interfaces – die Benutzerschnittstelle der Zukunft?. Katarina Stanoevska-Slabeva. Wirtschaftsinformatik & Management 10(6): 26-37 (2018)
- Chatbots and Conversational Interfaces: Three Domains of Use. Stefano Valtolina, Barbara Rita Barricelli, Serena Di Gaetano, Pietro Diliberto. CoPDA@AVI 2018: 62-70
- Developing Enterprise Chatbots – Learning Linguistic Structures. Boris Galitsky. Springer 2019, ISBN 978-3-030-04298-1, pp. 1-559
- Evaluations of an artificial intelligence instructor’s voice: Social Identity Theory in human-robot interactions. Chad Edwards, Autumn Edwards, Brett Stoll, Xialing Lin, Noelle Massey. Computers in Human Behavior 90: 357-362 (2019)
- Exploring requirements and opportunities of conversational user interfaces for the cognitively impaired. Matthias Baldauf, Raffael Bösch, Christian Frei, Fabian Hautle, Marc Jenny. MobileHCI Adjunct 2018: 119-126
- An overview of artificial intelligence based chatbots and an example chatbot application. Naz Albayrak, Aydeniz Ozdemir, Engin Zeydan. SIU 2018: 1-4
- The Short-term User Modeling for Predictive Applications. Michal Kompan, Ondrej Kassák, Mária Bieliková. Data Semantics 8(1): 21-37 (2019)
- Designing Conversational Interfaces to Reduce Dissonance. Conference on Designing Interactive Systems. Meira Chefitz, Jesse Austin-Breneman, Nigel Melville. Companion Volume 2018: 219-223
- Synthesis of model predictive control based on data-driven learning. Yuanqiang Zhou, Dewei Li, Yugeng Xi, Zhongxue Gan. CoRR abs/1904.01415 (2019)
- BBVA: Todo lo que puedes hacer con los asistentes virtuales (clic aquí)
- Luca – Telefónica: La revolución de chatbots, un fenómeno que ya está aquí (clic aquí)
- Accenture: Clientes bancarios listos para hablar (clic aquí)
- Forbes: Llegamos a la era de los bancos conversacionales (clic aquí)
- Telos – Fundación Telefónica: El nuevo mundo de los chatbots (clic aquí)
- Planteta Chatbot: Un rápido viaje hacia la Banca Conversacional (clic aquí)
- IPsoft: How Conversational Banking is Transforming the Customer Experience (clic aquí)
- CaixaBank: Asistente Virtual basado en inteligencia artificial para empleados (clic aquí)
Javier Porras Castaño
Experto en inteligencia artificial y científico de datos
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