The impact of AI : Opportunities and Risks of Artificial Intelligence in Finance.

Artificial intelligence is radically changing the way people interact with money. Everyone can benefit from the application of this technology. Not only financial institutions, but also bank clients and investors.

In this light Fintech shows a strong propensity to adopt the latest artificial intelligence technologies, with applications from quantitative trading to fraud detection, from credit management to audit and compliance. The similarities and affinities between artificial intelligence and fintech are enormous. Improved knowledge and social skills have pushed AI technology from the margins to the centre of the debate. It is therefore important to remark that artificial intelligence must be used in the most appropriate and ethical manner and that no AI algorithm in the world, no matter how sophisticated and high-performing, will be able to have all the information if it is not sufficiently fed by even “minimal human intervention”.

The so-called repetitive and/or redundant activities are such because essentially the AI is at the service of human work and, as such, must be used to carry out, control and possibly improve human work. Not to mention that factor called “sensitivity”, an undoubtedly human characteristic (quality) capable of steering and guiding the AI in the right direction. One example of this is “data automation/credit risk assesment”. Credit scores based on artificial intelligence are perhaps the most promising and relevant. In short, credit scoring is an assessment of how much a customer is able and willing to pay back debts. Artificial intelligence decisions on credit scores are based on many data, such as total income, credit history, transaction analysis, work experience, etc.

Credit scoring is a mathematical model based on statistical methods that considers a large amount of information. The result is that credit scoring, which uses artificial intelligence, provides sensitive, individual credit score assessments based on several additional factors in real time, potentially allowing more people with income to access finance.

Or even to detect possible “Banking Fraud” activity. Data are analysed using artificial intelligence-based analysis tools to detect suspicious transactions that could indicate fraud. Using artificial intelligence, it is possible to monitor user behaviour patterns and identify actions, most likely client profiles, that deviate from the norm and could indicate fraud attempts or incidents. Or “Automated customer service”. Customer service and relationship management is another critical area where artificial intelligence technologies provide a tangible value. Fintech companies can use chatbots to quickly answer customer questions and improve their overall experience with their products and services. These chatbots make it possible to personalise customer service and provide expert advice at low cost. In addition, chatbots can be available 24/7 without ever taking days off. Virtual assistants can also help customers navigate the bank’s offers, enhance user data and provide customised calls to action to increase targeted conversions.

Fintech, short for financial technology, refers to the use of technology to improve and automate financial services. This can include a wide range of applications and technologies, such as mobile banking applications, online payment platforms and investment advice based on artificial intelligence. The goal of fintech is to make financial services more efficient, accessible and user-friendly. It is for this reason that Artificial intelligence (machine learnings) is increasingly being used in the fintech sector because they have the potential to improve the efficiency and accuracy of financial services. Some of the main ways in which AI (machine learnings) is being used in the fintech sector are, for example, “process automation”. AI and machine learning can be used to automate repetitive tasks and processes, such as data entry and analysis. This can help reduce the amount of time and effort required to complete such tasks, as well as reduce the risk of errors or to improve “decision-making”.

Artificial intelligence and machine learning can also be used to make more accurate predictions and decisions. For example, machine learning algorithms can be trained on large datasets to identify patterns and trends that can be used to make more informed decisions on issues such as credit risk or fraud detection or even improve the customer experience. Artificial intelligence can improve the customer experience by providing personalised recommendations and advice. For example, AI-powered chatbots can be used to provide customers with instant answers to their questions, while machine learning algorithms can be used to provide personalised investment recommendations.

Overall, the use of AI (machine learnings), in the fintech sector, has the potential to improve the efficiency and accuracy of financial services and can also help improve the customer experience.

One of the main ways in which fintechs promote financial inclusion is by making financial services more accessible to under served communities. For example, by offering mobile banking services. Fintech companies can make it easier for people living in remote or rural areas to access financial services, such as transferring money or paying bills. This can be especially important for people who do not have access to traditional banks, the so called “unbanked”. Furthermore, fintech companies, through artificial intelligence, can also offer “customised” financial products and services “tailored” to the specific needs of the “underserved” communities, such as microloans or savings accounts with low minimum balances. This can help empower individuals and households to take control of their finances and improve their economic well-being.

In this sense, the combination of advanced technological infrastructures, a highly educated workforce, and a strong culture of innovation can facilitate the rapid adoption of fintech solutions by consumers and businesses, thanks to the combined use of technologies such as Blockchain (DLTs), IoT (Internet of Things), AI, and machine learning, together with the recently enacted Artificial Intelligence Act (AI Act) regulation, which can address any kind of abuse or reckless use of the technology under discussion. This is capable of building a profile of the customer, not only in his habits and preferences in accessing the service, but also in what are the particularly relevant “ethical” risks, personalising the same risk profile and thus being able to select the type of activity and/or operation to be performed, through the use of machine learning, which will allow, from time to time, to learn and therefore calibrate the risk profile for each customer/user through a transparent and immutable transactional process, the blockchain.

Artificial Intelligence is about to have a strong impact on the financial sector and especially on the Fintech sector, greatly increasing the benefits of the use of technology and consequently the number of users, for example the so called “unbanked people”. Fintech, in fact, is about to have a significant impact on society, both in terms of the way financial services are provided and in terms of the economy at large. To this must be added the activity of digital payments and money transfers. Fintech has already had a big impact on the way people make payments and money transfers and this trend is set to continue (e.g. mobile banking and peer-to-peer payment platforms).

And finally Blockchain and cryptocurrencies. Blockchain technology and cryptocurrencies (stablecoins) have the potential to revolutionise the financial sector and we can expect to see further innovations in this area in the coming years. Indeed, blockchain technology is set to impact the financial sector globally. It, in its architecture, is poised to enable faster and more convenient processing of financial transactions. Supply chain finance, for example, is one of the most revolutionary tools available to the financial industry, especially the fintech industry. Its main contribution is to simplify the integration of physical and financial flows. This is due to Blockchain technology and IoT. So is the automation of the process, leveraging artificial intelligence (AI) and Big Data Analytics. In addition, these technologies can help reduce many financial risks in the better known supply chain. Among these, we can find operational risks. For example, the risk of double financing or the risk of not getting the desired output.

Governments (regulators), companies (MNEs and SMEs), especially those that are highly automated, and fintech start-ups form an ecosystem. All participants in this ecosystem face different challenges and opportunities. This makes the landscape more dynamic and complex, as well as constantly evolving. Businesses can facilitate the exchange of non-sensitive data to create algorithms “digital twins” capable, through the use of AI, typically machine learnings, of profiling not only users, but so-called “best practices” that can develop and grow the “digital” ecosystem (regulatory sandbox) in which regulators, fintech companies and SMEs operate to reduce capital procurement costs, reduce legal uncertainty, via smart contracts, and support faster and more immediate “flow” of financial funding, the “working capital” to businesses.

Supply chain management involves the flow of goods and products from the initial to the final stage. Being an important part of many industries, the proper functioning of a supply chain is crucial for businesses. It is, therefore, very important for financial institutions and experts to understand the role of disruptive technologies in order to benefit from this financial revolution. The creation of a digital ecosystem through eIDAS II Regulation refers to a range of services that include verifying the identity of individuals, through AI tools, and online businesses and verifying the authenticity of electronic documents.

The main fears are essentially those of a massive recourse to artificial intelligence not only in the financial activities seen and listed above, but also in everyday activities, so to speak, which would tend to heavily dematerialise human activity to the detriment of a “trust” framework of absolute importance and, so to speak, crucial to narrate and build a “digital” ecosystem capable of meeting the needs of a “supply chain” that is now global and increasingly based on the immediate circulation of information. The client-institute relationship is destined to change radically. Paradoxically, it will be more inclusive and about to be even more customized. In other words, the introduction of artificial intelligence and machine learning will tend to replace human activity in what are, so to speak, repetitive and/or redundant activities or operations. These low value-added activities will henceforth focus on analysis and due diligence activities, always supported by the use of AI.

Antonio Lanotte is a Chartered Tax Adviser and Senior Auditor, International Tax Advisor and Business Consultant, Of Counsel Deotto Lovecchio & Partners and in the BOA of the Vernewell Group.

He is a GBBC Ambassador for Italy, a Member of the Dynamic Coalition on Blockchain Assurance and Standardization (DC-BAS) , a Member of the Panel of Experts at EUBOF (European Commission), a Member of the Advisory Council at Blockchain for Europe, and a Member of the Scientific Committee at Italia Fintech .