Nowadays, artificial intelligence (AI) has become an integrated part of our everyday life. Examples like the personal assistants on mobile phones or the autonomous vehicles may prove that artificial intelligence has managed to reduce human intervention and improve the driving experience respectively. Therefore, AI solutions are undoubtedly been simplifying human’s lives both at personal and professional level.
Human intelligence can potentially be enhanced and supported by AI in the working environment. A subset of AI, Machine Learning (ML), has the capability to consume and analyse huge volume of data and provide the users with useful insights. ML algorithms uncover hidden patterns in the datasets and support humans with accurate predictions, allowing them to take important and informed decisions shortly. These kind of algorithms can be a part of automated procedures or embedded within technological solutions. In the near future, autonomous systems will have the ability to learn from the cognitive behaviour of humans and demonstrate approximately the same behaviour under different circumstances and parameters.
AI techniques can be better developed in a structured environment, where the necessary information and objectives are well defined. In such cases, the accuracy of an AI model is significantly higher than before and it can exceed human judgment in specific cases (narrow AI). The classification of the emails between safe or malicious is an example of this type of AI. On the other hand, if the required information or the business objective is not completely clear, Intelligence Augmentation (IA) comes in and has a key role. Human intelligence can be enhanced by technology through the IA approach. Human has a centric role in the interaction with the machine. For example, a car collision avoidance system warns the driver to avoid an upcoming car accident with the provision of advanced information to enable proactive actions. However, in this case the driver still acts as the main user of the system by utilising the insights from IA to prevent the accident.
The difference between IA and AI is that IA aims to enhance human’s level of intelligence so that human could take an action or decision being wiser than before, using similar technologies to AI. On the other hand, AI tries to learn and mimic human cognitive behaviour. As an example for the IA case, a manager of a marketing department of an organisation can be aware when there is high probability for a specific group of customers to leave the organisation. At the same time, IA technologies support the user by recommending a series of potential actions to prevent this negative event. Furthermore, IA can successfully combine the results of AI algorithms and recommend specific products to a group of customers, considering both proper timing and their personal needs. Therefore, customer experience is being enhanced and customer lifecycle is extended.
A large number of financial institutions are already adopting AI technologies to support their operations. In some cases they use advanced algorithms to identify relationships and trends in the data. This enables them to move from the descriptive analysis of data to a more predictive and proactive approach. For example, algorithms like these can detect the fraudulent transactions based on a large number of parameters and reject them even before their execution. Another use of AI within financial institutions is the estimation of the credit score of their customers at a high level of accuracy, which can lead to the minimisation of the operating risk of the institution. Furthermore, AI is introducing new channels of communication with customers referring to the case of Chatbots. Chatbots incorporate various Natural Language Processing and Generation algorithms in order to be able to understand and communicate with customers in a similar way to human, thus saving time and costs.
Organisations adopt a series of AI algorithms, having a number of different results most of the time. It is up to the user to understand and utilise the results and take a decision or an action. IA can easily recommend actions derived by the results of AI. It can also estimate the outcome of an action, giving the user the opportunity to select the one with the most profitable result. Those examples demonstrate the important role of AI and IA in the financial institutions for both employees and customers.
IA has a crucial role in the enhancement of human intelligence. The user is being provided with combined information derived from different AI models and data sources that were not available before. Based on this new insights enabled by IA, human has the opportunity to take better informed and as a result efficient and profitable decisions. There is no competition between AI and IA. However, both AI and IA will be having a key role in automation in the near future.
Constantinos Valanides, Senior Advisor II, KPMG Limited,
© 2020 KPMG International Cooperative (“KPMG International”), a Swiss entity. Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm. All rights reserved.
Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm.