To deliver a seamless customer experience, consistent across all channels – digital or direct – a financial firm can leverage ML to facilitate a granular approach. As adoption increases, the future trends in finance AI include fraud detection, customer service automation, and improved credit scoring. Regulatory compliance is another area where AI technologies make a big difference in finance.
- This is particularly important for those SMEs that are viable but unable to provide historical performance data or pledge tangible collateral and who have historically faced financing gaps in some economies.
- Even the popular ChatGPT, a natural language processing (NLP) based AI technology, is a prime example of the future of finance.
- Such tools can also be used in high frequency trading to the extent that investors use them to place trades ahead of competition.
For example, it has implemented a proprietary algorithm to detect fraud patterns—each time a credit card transaction is processed, details of the transaction are sent to central computers in Chase’s data centers, which then decide whether or not the transaction is fraudulent. Chase’s high scores in both Security and Reliability—largely bolstered by its use of AI—earned it second place in Insider Intelligence’s 2020 US Banking Digital Trust survey. Its offerings costing methods and important costing terms include checking and savings accounts, small business loans, student loan refinancing and credit score insights. For example, SoFi members looking for help can take advantage of 24/7 support from the company’s intelligent virtual assistant. The platform lets investors buy, sell and operate single-family homes through its SaaS and expert services. Additionally, Entera can discover market trends, match properties with an investor’s home and complete transactions.
Scienaptic AI provides several financial-based services, including a credit underwriting platform that gives banks and credit institutions more transparency while cutting losses. Its underwriting platform uses non-tradeline data, adaptive AI models and records that are refreshed every three months to create predictive intelligence for credit decisions. Aggressive companies who anticipated the waves are already realizing improvements in their operations and profits, so it may be crunch time for fence-sitting institutional investors to keep up by integrating AI into their businesses. Predictive analytics, for instance, helps institutional investors digest massive amounts of data across several metrics, providing better clarity on market trends and refining asset allocation strategies. Similarly, machine learning algorithms increase adaptability, allowing institutional investors to react quickly to shifting market conditions.
AI Fuels Algorithmic Investing for Quicker, More Efficient Transactions
The search engine provides brokers and traders with access to SEC and global filings, earning call transcripts, press releases and information on both private and public companies. Time is money in the finance world, but risk can be deadly if not given the proper attention. To be sure, Nvidia’s investment in SoundHound amounted to pocket change for the huge GPU maker.
Instead, what is currently observed is the use of specific AI applications in blockchain-based systems (e.g. for the curation of data to the blockchain) or the use of DLT systems for the purposes of AI models (e.g. for data storage and sharing). AI techniques could further strengthen the ability of BigTech to provide novel and customised services, reinforcing their competitive advantage over traditional financial services firms and potentially allowing BigTech to dominate in certain parts of the market. The data advantage of BigTech could in theory allow them to build monopolistic positions, both in relation to client acquisition (for example through effective price discrimination) and through the introduction of high barriers to entry for smaller players. 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. In addition, the use of algorithms in trading can also make collusive outcomes easier to sustain and more likely to be observed in digital markets (OECD, 2017[16]).
Suitability requirements, such as the ones applicable to the sale of investment products, might help firms better assess whether the prospective clients have a solid understanding of how the use of AI affects the delivery of the product/service. To date, there is no commonly accepted practice as to the level of disclosure that should be provided to investors and financial consumers and potential proportionality in such information. The largest potential of AI in DLT-based finance lies in its use in smart contracts11, with practical implications around their governance and risk management and with numerous hypothetical (and yet untested) effects on roles and processes of DLT-based networks. As such, many of the suggested benefits from the use of AI in DLT systems remains theoretical, and industry claims around convergence of AI and DLTs functionalities in marketed products should be treated with caution.
Synthetic datasets can also allow financial firms to secure non-disclosive computation to protect consumer privacy, another of the important challenges of data use in AI, by creating anonymous datasets that comply with privacy requirements. Traditional data anonymisation approaches do not provide rigorous privacy guarantees, as ML models have the power to make inferences in big datasets. The use of big data by AI-powered models could expand the universe of data that is considered sensitive, as such models can become highly proficient in identifying users individually (US Treasury, 2018[32]). Facial recognition technology or data around the customer profile can be used by the model to identify users or infer other characteristics, such as gender, when joined up with other information.
Investment and Portfolio Management
Distributed ledger technologies (DLT) are increasingly being used in finance, supported by their purported benefits of speed, efficiency and transparency, driven by automation and disintermediation (OECD, 2020[25]). Major applications of DLTs in financial services include issuance and post-trade/clearing and settlement of securities; payments; central bank digital currencies and fiat-backed stablecoins; and the tokenisation of assets more broadly. Merging AI models, criticised for their opaque and ‘black box’ nature, with blockchain technologies, known for their transparency, sounds counter-intuitive in the first instance. At the single trader level, the lack of explainability of ML models used to devise trading strategies makes it difficult to understand what drives the decision and adjust the strategy as needed in times of poor performance. Given that AI-based models do not follow linear processes (input A caused trading strategy B to be executed) which can be traced and interpreted, users cannot decompose the decision/model output into its underlying drivers to adjust or correct it.
83% of millennials and 79% of Generation Z respondents said they would trust a robot over their organization’s finance team. Millennial employees are nearly four times more likely than Baby Boomers to want to work for a company using AI to manage finance. KPMG has market-leading alliances with many of the world’s leading software and services vendors.
2.2. Algorithmic Trading
Skills, such as business strategy, leadership, risk management, negotiation, and data-based communication and storytelling, will help to complement the abilities of AI in finance. 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. Although a convergence of AI and DLTs in blockchain-based finance is promoted by the industry as a way to yield better results in such systems, this is not observed in practice at this stage. Increased automation amplifies efficiencies claimed by DLT-based systems, however, the actual level of AI implementation in DLT-based projects does not appear to be sufficiently large at this stage to justify claims of convergence between the two technologies.
Some regulators require, in some instances, the evaluation of the results produced by AI models in test scenarios set by the supervisory authorities (e.g. Germany) (IOSCO, 2020[39]). For example, AI can be a powerful tool to optimise windmill operations and safety, analyse traffic patterns in transportation, and improve operations in energy grids. Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that question the status quo and spark change. BCG delivers solutions through leading-edge management consulting, technology and design, and corporate and digital ventures. We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place. But, the adoption of generative AI in finance functions entails challenges, including accuracy and data security and privacy.
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The lack of explainability also means that lenders have limited ability to explain how a credit decision has been made, while consumers have little chance to understand what steps they should take to improve their credit rating or seek redress for potential discrimination. Importantly, the lack of explainability makes discrimination in credit allocation even harder to find (Brookings, 2020[20]). The use of AI and big data has the potential to promote greater financial https://accountingcoaching.online/ inclusion by enabling the extension of credit to unbanked parts of the population or to underbanked clients, such as near-prime customers or SMEs. This is particularly important for those SMEs that are viable but unable to provide historical performance data or pledge tangible collateral and who have historically faced financing gaps in some economies. Ultimately, the use of AI could support the growth of the real economy by alleviating financing constraints to SMEs.
While algorithmic trading isn’t new—stock exchanges started using computerized trading nearly half a century ago—AI is supercharging the practice with advanced capabilities, empowering institutional investment firms to save time and broaden their business missions. While banks have not yet adopted AI in their algorithmic trading strategies, it’s very likely that as the competitive advantages become apparent, innovation and adoption will take place. Even the popular ChatGPT, a natural language processing (NLP) based AI technology, is a prime example of the future of finance. This technology offers conversation-based automated customer service and even generates financial advice. For example, New York-based startup Kensho Technologies offers various AI-based services for financial institutions, including algorithmic trading and risk analysis tools. Such models can predict future market trends based on past data, allowing businesses to make more informed decisions and increase profitability.