The Future of Artificial Intelligence in Finance: Opportunities and Challenges

Image analysis and various administrative tasks, such as filing, and charting are helping to reduce the cost of expensive human labor and allows medical personnel to spend more time with the patients. For example, algorithms can be used to analyze the creditworthiness of loan applicants, taking into account factors such as credit score, income level, and so on. By identifying patterns and trends, AI systems can predict the likelihood of a borrower defaulting on their loan. Traditional processes are time consuming and can lead to delayed payments, while the use of AI in the accounts payable process can help companies manage and process invoices in a fast, effective, and transparent manner. NLP or natural language processing is the branch of AI that gives computers the ability to understand text and spoken words in much the same way human beings can.

In terms of order flow management, traders can better control fees and/or liquidity allocation to different pockets of brokers (e.g. regional market-preferences, currency determinations or other parameters of an order handling) (Bloomberg, 2019[7]). Fintech is driving global innovation that is transforming how companies, consumers, and money interact across industries, from financial services to retail to transportation and beyond. Utilizing AI, fintech interactions are more personalized with recommendation engines, self-service is enhanced via conversational AI, and transactions are more secure with the use of deep learning fraud detection models. Sentiment analysis is an application of natural language processing in which analysis of news and monitoring of social media are used to build sentiment measures of the financial market, which can be used to drive real-time trading decisions. Other applications include assessing counterparty credit risk and analyzing surveys to understand why customers are satisfied or dissatisfied. In fact, a recent study found that AI algorithms outperformed traditional rule-based systems by up to 20% in detecting fraudulent credit card transactions.

On the side of customers, AI might be able to resurrect their failing trust in banks and financial institutions. A study has revealed that consumer trust in financial service providers has dropped from pre-pandemic 43% to an all-time low of 29% post-COVID 19 pandemic. Consumers drive the change to digital solutions and automation, which will ultimately spill over into how things are done in B2B companies as well. It’s difficult to overestimate the impact of AI in financial services when it comes to risk management. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyze the history of risk cases and identify early signs of potential future issues.

What is cloud-based finance?

In particular, AI techniques such as deep learning require significant amounts of computational resources, which may pose an obstacle to performing well on the Blockchain (Hackernoon, 2020[29]). It has been argued that at this stage of development of the infrastructure, storing data off chain would be a better option for real time recommendation engines to prevent latency and reduce costs (Almasoud et al., 2020[27]). Challenges also exist with regards to the legal status of smart contracts, as these are still not considered to be legal contracts in most jurisdictions (OECD, 2020[25]). Until it is clarified whether contract law applies to smart contracts, enforceability and financial protection issues will persist.

• No one wants to stumble upon a multi-thousand dollar transaction they did no make, nor does the bank want to cover the damages of a theft.
• Our partners are here to assist your organization at every level to build and execute transformative AI strategies, products, and services.
• So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important.
• The advent of ERP systems allowed companies to centralize and standardize their financial functions.

Access on-demand content from Money 20/20 to hear from leaders in the industry and experience AI demos in the product innovation area. Learn how top players in the banking world are using AI, how their efforts have been impacted by the pandemic, and who should be doing what to further the use of this technology. The NVIDIA RAPIDS Accelerator for Apache Spark accelerates processing time up to 5X or more and allows the same work to be completed with 4X less infrastructure cost.

Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report. Interestingly, AI applications risk being held to a higher standard and thus subjected to a more onerous explainability requirement as compared to other technologies or complex mathematical models in finance, with negative repercussions for innovation (Hardoon, 2020[33]). The objective of the explainability analysis at committee level should focus on the underlying risks that the model might be exposing the firm to, and whether these are manageable, instead of its underlying mathematical omni calculator logo promise. A minimum level of explainability would still need to be ensured for a model committee to be able to analyse the model brought to the committee and be comfortable with its deployment. The human parameter is critical both at the data input stage and at the query input stage and a degree of scepticism in the evaluation of the model results can be critical in minimising the risks of biased model decision-making. Human judgement is also important so as to avoid interpreting meaningless correlations observed from patterns as causal relationships, resulting in false or biased decision-making.

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Having said that, the financial industry is one in which AI is playing a particularly important role. We’ll go over a number of ways that artificial intelligence (AI) has altered the financial game in recent years, from providing excellent fraud detection and financial risk management to fully revolutionizing the banking sector. With AI poised to handle most manual accounting tasks, the development and proficiency of higher-level skills will be imperative to success for the next generation of finance leaders. Finance professionals will still need to be proficient in the fundamentals of finance and accounting to oversee the algorithms and be able to spot anomalies.

This includes tasks such as portfolio construction, risk management, and investment decision-making. Shapeshift is a decentralized digital crypto wallet and marketplace that supports more than 750 cryptocurrencies. The platform provides users access to nine different blockchains and eight different wallet types. ShapeShift has also introduced the FOX Token, a new cryptocurrency that features several variable rewards for users. Vectra offers an AI-powered cyber-threat detection platform, which automates threat detection, reveals hidden attackers specifically targeting financial institutions, accelerates investigations after incidents and even identifies compromised information.

Nevertheless, the introduction of AI in DLT-based networks does not necessarily resolve the ‘garbage in, garbage out’ conundrum as the problem of poor quality or inadequate data inputs is a challenge observed equally in AI-based applications. Potential consequences of the use of AI in trading are also observed in the competition field (see Chapter 4). Traders may intentionally add to the general lack of transparency and explainability in proprietary ML models so as to retain their competitive edge.

Moreover, deep learning requires a substantial historical training data set to build a robust and accurate predictive model. For example, nonlinearities in oil price distribution such as volatility are captured by neural network models. A leading financial firm, JP Morgan Chase, has been successfully leveraging Robotic Process Automation (RPA) for a while now to perform tasks such as extracting data, comply with Know Your Customer regulations, and capture documents.

Depending on how they are used, AI algorithms have the potential to help avoid discrimination based on human interactions, or intensify biases, unfair treatment and discrimination in financial services. The risk of unintended bias and discrimination of parts of the population is very much linked to the misuse of data and to the use of inappropriate data by ML model (e.g. in credit underwriting, see Section 1.2.3). AI applications can potentially compound existing biases found in the data; models trained with biased data will perpetuate biases; and the identification of spurious correlations may add another layer of such risk of unfair treatment (US Treasury, 2018[32]).

3.4. Training, validation and testing of AI models to promote their robustness and resilience

While these systems automate financial processes, they require significant manual maintenance, are slow to update, and lack the agility of today’s AI-based automation. Unlike rule-based automation, AI can handle more complex scenarios, including the complete automation of mundane, manual processes. In this article, we’ll go over the top 7 AI tools for finance teams and how they are reshaping the finance industry by streamlining processes and eliminating manual work. From financial data analysis to budgeting and forecasting, accounting, and tax and compliance, these advanced tools empower finance teams to focus on strategic decision-making and value-added activities.

Fraud detection is one of the key areas where AI can provide significant support to finance departments. Artificial intelligence can be used to analyze large datasets and identify fraudulent activities – such as credit card fraud or money laundering – in real-time. Simplify your financial close process by leveraging machine learning to identify and resolve discrepancies between intercompany transactions.

The Impact of AI on Financial Services

Accelerating data analysis allows businesses to achieve results faster while being more cost-effective. Many financial institutions are turning to machine learning to boost fraud detection accuracy, reduce false positives, and improve compliance with AML and KYC regulations. For example, AI will automate and improve manual processes such as portfolio management and ensure customers have access to 24/7 service.

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The difficulty in decomposing the output of a ML model into the underlying drivers of its decision, referred to as explainability, is the most pressing challenge in AI-based models used in finance. In addition to the inherent complexity of AI-based models, market participants may intentionally conceal the mechanics of their AI models to protect their intellectual property, further obscuring the techniques. The gap in technical literacy of most end-user consumers, coupled with the mismatch between the complexity characterising AI models and the demands of human-scale reasoning further aggravates the problem (Burrell, 2016[37]).

The most disruptive potential of AI in trading comes from the use of AI techniques such as evolutionary computation, deep learning and probabilistic logic for the identification of trading strategies and their automated execution without human intervention. Contrary to systematic trading, reinforcement learning allows the model to adjust to changing market conditions, when traditional systematic strategies would take longer to adjust parameters due to the heavy human involvement. Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. KAI helps banks reduce call center volume by providing customers with self-service options and solutions.

Examples of AI in Finance

One way it uses AI is through a compliance hub that uses C3 AI to help capital markets firms fight financial crime. Announced in 2021, the machine learning-based platform aggregates and analyzes client data across disparate systems to enhance AML and KYC processes. FIS also hosts FIS Credit Intelligence, a credit analysis solution that uses C3 AI and machine learning technology to capture and digitize financials as well as delivers near-real-time compliance data and deal-specific characteristics. Finance is defined as the management, creation, and analysis of money and investments. Certain aspects of banking and finance are undertaken by dedicated financial institutions, such as credit scoring, underwriting decisions, and fraud detection. Other areas are managed internally by organizations, such as risk assessment, budgeting, and planning investments.

This is the technology that underpins image and speech recognition used by companies like Meta Platforms (META 2.31%) to screen out banned images like nudity or Apple’s (AAPL 2.19%) Siri to understand spoken language. In this instructor-led, hands-on workshop, learn how to quickly build and deploy production-quality conversational AI applications with real-time transcription and natural language processing capabilities using NVIDIA Riva framework. Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement. Ernst & Young has reported a 50%-70% cost reduction for these kinds of tasks, and Forbes calls it a “Gateway Drug To Digital Transformation”.

When it comes to credit risk management of loan portfolios, ML models used to predict corporate defaults have been shown to produce superior results compared to standard statistical models (e.g. logic regressions) when limited information is available (Bank of Italy, 2019[17]). AI-based systems can also help analyse the degree of interconnectedness between borrowers, allowing for better risk management of lending portfolios. Ensure financial services providers have robust and transparent governance, accountability, risk management and control systems relating to use of digital capabilities (particularly AI, algorithms and machine learning technology). AI in trading is used for core aspects of trading strategies, as well as at the back-office for risk management purposes. When used for risk management purposes, AI tools allow traders to track their risk exposure and adjust or exit positions depending on predefined objectives and environmental parameters, without (or with minimal) human intervention.

Ideally, users and supervisors should be able to test scoring systems to ensure their fairness and accuracy (Citron and Pasquale, 2014[23]). Tests can also be run based on whether protected classes can be inferred from other attributes in the data, and a number of techniques can be applied to identify and/or rectify discrimination in ML models (Feldman et al., 2015[36]). The G20 Riyadh Infratech Agenda, endorsed by Leaders in 2020, provides high-level policy guidance for national authorities and the international community to advance the adoption of new and existing technologies in infrastructure. That said, some AI use-cases are proving helpful in augmenting smart contract capabilities, particularly when it comes to risk management and the identification of flaws in the code of the smart contract. AI techniques such as NLP12 are already being tested for use in the analysis of patterns in smart contract execution so as to detect fraudulent activity and enhance the security of the network. Importantly, AI can test the code in ways that human code reviewers cannot, both in terms of speed and in terms of level of detail.