In recent years, financial institutions have invested in new artificial intelligence (AI) skills which mainly apply to natural language processing solutions. The goal is to implement virtual assistants who talk naturally with users, but also other solutions, such as image recognition for process automation, along with deep learning techniques applied to security and fraud detection.
Fortunately, most of them have left behind the early stages of implementation and the testing phase of initial projects. The challenge of the next few years is to manage scaling AI capabilities inside companies, as artificial intelligence also applies to most processes of those financial firms, from selecting employees, for example, to creating new businesses.
The context of adopting AI is highly complex and it must be reminded that this technology is permanently improving and part of a complex ecosystem, requiring the definition and execution of a specific corporate strategy involving multiple players, such as fintechs, cloud computing providers and open source frameworks. It also includes the adoption of new methods and processes required to create new functions in business and technology, recruiting talented professionals (hard to find in the market) and changes in corporate culture.
However, the adoption of AI requires immediate investments, since it is going to be an essential tool for financial institutions to effectively connect with their customers, taking into account the impact of open banking and offering high value-added services and products in line with global trends, such as the redefinition of customer service. This is already taking place today in the form of bots and virtual assistants for multichannel connection with customers via web, mobiles, WhatsApp, etc. The next stage will include offering omnichannel cognitive services that integrate different channels, cognitive IVRs, cognitive call centers, smarter assistants and more proactive contacts.
The natural evolution will include personalization based on using all available customer information, AI capabilities and the records of financial and transaction data, services and social networks to devise services and adapt them to people’s profiles and specific needs. On this front, we find a broad set of solutions, such as digital personal financial management consultants, investment advisors and advanced risk scores for different credit granting profiles. To summarize, solutions that integrate business opportunities among customers or the creation of contextual user experiences.
Another trend is the adoption of the Phygital concept, which aims to make physical service spaces more effective, interactive and friendly, as they are tailored to specific customer needs and similar to the digital environment of institutions. Digital transformation of branches will require the use of AI features, such as face and image recognition technologies, full environmental monitoring, cognitive totems, or tools used to customize interactions.
A wide range of benefits will result for banks and financial businesses, as well as for consumers. However, in the near future, the massive use of AI may pose new risks that will need to be mitigated by institutions and require bias analyses for machine learning solutions to meet compliances, using techniques to make models explicable, as well as constant monitoring of the evolution of adversarial attacks.
Given that scenario, financial institutions will require the support of consulting firms specializing in AI, such as everis, to define sound strategies that allow adopting these technologies, as well as to keep up the constant development of artificial intelligence solutions to meet new and upcoming challenges.