The finance industry landscape is evolving at an alarming rate – gone are the days of in-person interactions at bricks and mortar financial institutions, the implementation of new technology is the way forward and AI is the driving force behind it.
To understand the benefits that AI can bring to the finance sector, we first need to look at the different types of technology it encompasses:
Machine Learning is the development of systems that can learn and carry out processes without the need for human prompting.
Natural Language Processing is an AI technology that allows computers to interpret and comprehend human language (both speech and text).
Synthetic Data Production is a way of creating artificially generated data that mimics the patterns and features of real-world data but without identifiable markers.
Applications of AI In Finance
The applications of AI systems in finance are incredibly vast and will only continue to grow as the technology develops. Some of the most current and commonly applicable uses for day-to-day implementation include:
Fraud is a multi-billion dollar issue for the finance industry but with the help of AI and, in particular, machine learning, there is the potential to significantly reduce these losses.
AI can be fed historical transaction data to then develop an algorithm to better identify anomaly transactions.The more AI is deployed and utilized in this way, the better the algorithms will become at detecting fraudulent transactions.
The use of synthetic data can also play a massive role in helping to tackle fraud across all branches of the financial sector.
Raw financial data (and the sharing of it) is subject to all manner of regulations and privacy laws, meaning that collaboration between different financial institutions and industries to help tackle fraud has been near impossible in the past. AI can be used for synthetic data generation at scale to create anonymized data that preserves the key characteristics and features from the original sensitive data sets, to train and develop more precise fraud detection models.
Risk assessment is something that has already been partly computerized in financial institutions, but AI can help to make processes even more efficient.
Tasks such as checking the borrowing eligibility of applicants and assigning risk profiles are a fairly automated process for lower-value products (personal and automotive loans, for example) but manual underwriting is still required for larger financial agreements.
AI can be used to create more precise and specialized risk profiles and give underwriters in-depth analytical data presented in a straightforward format, to speed up the decision process and mitigate the risk of defaults.
Portfolio Management and Trading
High-level trading and portfolio management is a serious business, whole markets can depend on the decisions that are being made and mistakes can cost astronomical amounts of money. There needs to be trust in the decisions that are being made, and while there is no doubt that the people calling the shots on these trades and exchanges have a nuanced understanding of the history and trends of the market they are looking at, they can only process and evaluate a certain amount of data at one time.
This is where AI can really come into its own – training a system on decades worth of financial and economic data can create algorithms that can identify and evaluate trends on a far more intricate level than a human ever could. Trades can also be executed more quickly and precisely by systems that are constantly monitoring the market, primed to identify the ideal moment based on numerous in-depth calculations and specific parameters.
24/7 Customer Service
Now that the majority of our banking can be done from our phones at any time of the day, financial institutions need to have a constant customer service presence. By employing the use of natural language processing in the form of AI chatbots, banks can reduce the amount of call center and office-based customer support representatives they need.
Benefits of AI in Finance
The financial and global economic landscape has changed a lot over recent years. By utilizing the many various applications of AI, companies will find not only new opportunities for growth and profitability but also the innovation needed to help keep them relevant in an exceedingly competitive market.
Even if AI isn’t used to complete highly complex work, the amount of time that can be saved by using it to handle basic tasks adds up very quickly.
AI chatbots can speed up customer service processes, not only are they available 24/7, but they can also ensure queries that need to be handled by a human end up in front of the right person with all the information required in a matter of minutes.
Financial auditing and forensic accounting can involve sifting through large amounts of data and records, sometimes taking months to complete. Using AI to streamline processes and organize data is far quicker and more efficient than a human could manage to be. Machine learning can be used to collate and organize financial records ready for auditors to analyze, with algorithms highlighting any anomalous data that warrants further investigation.
Improved Accuracy and Efficiency
Errors and mistakes made by humans in financial settings can have serious costs and implications. AI-based systems are much more precise and efficient at making complex decisions and calculations.
The production and sharing of synthetic data sets across various sectors of the industry (that previously had no way to collaborate due to information privacy restrictions) will mean algorithms can be trained to become more adept at picking up transaction patterns that indicate fraud.
The advances that AI can bring to risk management across all sectors of the finance industry could save a vast amount of money. In fact, it’s predicted that by employing AI automation in middle office tasks, such as fraud and risk mitigation, could save North American banks close to $70 billion by 2025.
The Challenges of Implementing AI in Finance
The benefits that AI technologies can bring to the finance industry are massive, but there are also some challenges to consider with its implementation.
AI algorithms have great potential to improve accuracy and reduce errors, but this is dependent on them being ‘taught’ or fed with accurate data when being developed. The synthetic data sets that are being used for the development of AI models will need to be strictly checked to ensure it is free from any errors but also representative of the customer base it is intended to be used for, and free from any biased data.
The finance industry is built on a strict set of regulatory codes and laws to help keep sensitive data safe. Ensuring that AI models adhere to these regulatory requirements can be a very involved and time consuming process but it is imperative to make sure that customers’ data is safeguarded. For auditing purposes, AI models and the way in which they build/produce data will need to be explainable and transparent in their work or calculations – the more complex an AI model is, the harder this will be to demonstrate, but it is still a legal requirement and needs to be adhered to.
Future of AI in Finance
The use of AI in finance has undeniable benefits and almost unlimited potential. Companies that are slow on the uptake of these new technologies will find the gap between them and early adopters growing ever more apparent, as customers gravitate towards the efficiency these innovations introduce.
With the boom in FinTech startups disrupting the consumer market and even the oldest financial institutions around the world investing in AI systems and development, it’s evident that the adoption and integration of AI technology is what the future of the financial industry will be built on.