Artificial Intelligence in FinTech

Artificial Intelligence, short for AI, is a system that perceives the environment and collects data in order to make intelligent decisions. AI plays a vital role in FinTech, helping stream line operations, reduce risks of fraud, and providing personalized financial services to every individual. However, because Artificial General Intelligence (AGI) doesn’t exist as of yet, Artificial Narrow Intelligence (ANI) does, and that is what specialises in specific financial tasks.

There are several subfields within AI itself. These include the following:

It is where the computer itself learns from data to help recognise patterns, and make predictions and decisions. In terms of FinTech, ML can help in recognising fraud patterns, predict the future market trends, and analyse customer behaviour to make decisions.

It is a branch of ML and uses neural networks to tackle AI’s biggest challenges such as portfolio optimisation and risk modelling in FinTech.

This subfield takes AI a step further and enables it to understand and generate human languages such as text and speech. NLP can be quite useful in FinTech as it can help assist in generating financial reports and power chatbots to be able to answer finance related questions.

It gives AI the ability to “see” and understand images and objects. In Fintech, it can help with identity verification and fraud detection etc.

This brings AI into the real-world, allowing machines to interact with its surroundings. In financial services, robots can help streamline repetitive tasks such as multiple transactions.

This area helps AI systems understand information and make smart decisions. This can be very useful in helping make informed decisions regarding structured and unstructured financial data.

Artificial Intelligence (AI) has proven to be very useful to mankind as it has helped make day to day tasks easier and quick to solve. AI has been particularly useful and has shown many of the capabilities it contributes in the field of FinTech. Let’s take a look at some of the capabilities of AI in FinTech. In FinTech, AI contributes by forecasting the market movements and trends related to the market. Moreover, AI helps in managing large financial portfolios and in making smart investment strategies. It also allows financial services to detect traces of fraud in real-time. Additionally, it helps give automated financial reports, financial recommendations, approval for loans and delivers personalised services to each individual.

However, while AI does contribute significantly to FinTech, that does not mean that it does not have its limitations. First of all, AI often faces difficulty in handling new and unforeseen financial situations. Moreover, while a chatbot can be a great source of getting an individual’s questions answered, it still does not resonate deeply with humans as it lacks Emotional Intelligence and social nuance. Additionally, bias is also a big challenge in AI as it can produce unfair predictions and decisions if trained on biased data. Lastly, AI’s performance is only as good as the data it learns from, thus if data is not accurate and is low quality, the performance may not be great.

When a company builds AI-driven FinTech solutions, it is more likely to have a competitive advantage. Moreover, having AI-driven solutions also contributes to reducing operational cost, be it collecting value data for strategic decision-making, automating financial tasks, and optimising workflows. However, to build AI-driven organisations, there are certain requirements. Firstly, the company should build a roadmap for AI adoption by obtaining leadership support and a clear vision. Secondly, they should develop a data strategy plan to collect, utilise, and govern relevant data for AI initiatives. Thirdly, investment in scalable computing infrastructure and AI tools is a must, be it on-premise or with cloud providers. Fourthly, the company should hire qualified roles who have the expertise in AI and ML to handle AI-driven organisations in order to build FinTech solutions. Moreover, cross functional collaboration should be encouraged within different departments to add more input and value in innovative ideas. Additionally, the company should use AI developments to help drive business success by providing customer-centric financial services to increase revenue and build scalable solutions. Since AI is also a constantly evolving discipline, continuous learning and AI literacy should be promoted amongst the people in the company. Last but not least, everything within the company must be underpinned by policies for ethical and responsible use of AI and data, starting with robust security measures and principles for responsible and transparent AI systems to make sure all solutions made are within the ethical guidelines.

Once a company becomes successful in building AI-driven FinTech solutions, it is important to assess and closely monitor the success of the AI initiative not only during its development, but also after its deployment. To measure the success of AI-driven FinTech solutions within an organisation during its development phase, ML metrics are used to determine how well the AI is performing in producing solutions before actually deploying the AI model. Using classification, a portion of data is used to train the AI model to learn from different data features in helping produce ethical and accurate solutions. Once the AI model is deployed, its performance needs to be monitored closely. This is because the assessed metric often starts to deteriorate over time and this may result in inaccurate FinTech solutions for the organisation, making the AI model unreliable. In order to quantify the success of a business using an AI system to derive FinTech solutions, businesses often use KPIs also known as Key Performance Indicators. KPI is a measurable indicator of the performance and progress of specific objectives within an organisation.

AI is not inherently biased, but humans are biased by nature. It is our own biasness which unintentionally invades AI systems through the data used to train them or the design of the algorithm. For e.g. in FinTech companies, AI models that that been trained on biased historical data regarding the applications of loans may unjustly reject loan applicants from certain neighbourhoods or backgrounds, even if they are financially qualified. To address this bias, companies can start introducing techniques and metrics to promote diversity and fairness in the design and evaluation of the system. Moreover, Data privacy in FinTech companies is a must as sensitive and personal information must be safeguarded from unauthorised access and misuse. For this very reason companies must implement robust measures like fortifying data encryption protocols, anonymising sensitive data such as contact information or ethnicity, and adhering to key regulations like EU’s GDPR or California’s CCPA.

As AI continues to expand and pave its way in different work-force related industries, fear of job displacements has increased. The impact of AI in process automation is said to increasingly displace workers, and the rapid evolution of the field demands the acquisition of new human skills. Moreover, if AI systems are designed without considering human needs, then this may create problems in generating decisions from an AI model. Thus, for this reason, it is important to make the human-centric designs of AI models to ensure that their decisions complement the decisions of humans. Lastly, not many people are literate about how AI works and because of this, they are unsure about how to use these models safely. To tackle this issue, the company must provide its employees and stakeholders with the necessary AI knowledge for its safe use.

With the integration of AI in FinTech, it has allowed financial services to be more accessible to a wider range of the population as it offers digital tools that make banking and investment simple and easy. Moreover, AI in FinTech gives clearer information and smart insights to its users, supporting transparency in order to help people make smart and informed financial decisions.
Moreover, AI automates tasks in financial institutions, helping improve the overall efficiency of the routine tasks that are conducted. Moreover, it helps reduce operational costs, helping these companies earn higher profit. Additionally, through AI models, financial services can easily detect fraud by using AI fraud detection systems. AI in FinTech also makes accurate financial analysis and provides accurate decisions, ultimately strengthening risk management and contributing to revenue growth.
Additionally, AI in the workforce creates new opportunities in different fields such as data analysis, AI model development, cybersecurity, and ethical governance. Moreover, it also helps automate repetitive tasks, increasing overall efficiency. Additionally, with AI advancing more and more with each passing day, it is important for the employees to learn new skills in AI and adapt to how the AI-driven model works in order to be able to work in AI related roles.

AI learns from data using three means in Machine learning: Supervised learning, Unsupervised learning, and Reinforcement learning.

This type of learning has two categories: Classification and Regression. The supervised learning approaches like classification require pre-labelled data observation to learn from, that is, data instances whose classes are already known. For example, classifying transactions as “fraud” or “legitimate” or a group of customers as “high-risk” or “low-risk”. The other approach such as regression assigns each data observation a numerical output or label based on its inputs. For example, predicting the future stock prices or market trends.

This is the type of learning in which properties or patterns behind the data are learnt without being assigned to any predefined label. This includes clustering which is a technique used to find subgroups of similar data such as identifying customer segments. Another is anomaly detection which helps identify unusual data observations like suspicious credit card transactions.

This is where an AI agent is trained to solve complex problems such as optimising trading strategies and portfolio management through trial and error.

Areas where AI interact with the physical or digital environment include:

This is the kind of AI behind identity verification and compliance checks.

This is the type of AI that classifies texts, summarises it, and answers questions such as chatbots. In financial institutions, this can be particularly helpful in processing financial documents, customer queries, and sentiment analysis.

This type of AI is the combination of computer vision and NLP in a wide number of applications. In FinTech, robotics can help automate routine digital tasks such as transaction processing and updating customer records etc.