This is why finance will be one of the first areas to see the impact of these technologies on day-to-day activities—in everything from automating payments to calculating risk—with detailed analytics that automatically audit processes and alert teams to exceptions. Ayasdi creates cloud-based machine intelligence solutions for fintech businesses and organizations to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes. Its Sensa AML and fraud detection software runs continuous integration and deployment and analyzes its own as well as third-party data to identify and weed out false positives and detect new fraud activity. Kensho, an S&P Global company, created machine learning training and data analytics software that can assess thousands of datasets and documents.
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. Given that code is grant proposals or give me the money! the underlying basis of any smart contract, flawless coding is fundamental for the robustness of smart contracts. It should be noted that the massive take-up of third-party or outsourced AI models or datasets by traders could benefit consumers by reducing available arbitrage opportunities, driving down margins and reducing bid-ask spreads.
How is AI driving continuous innovation in finance?
As in other blockchain-based financial applications, the deployment of AI in DeFi augments the capabilities of the DLT use-case by providing additional functionalities; however, it is not expected to radically affect any of the business models involved in DeFi applications. Currently, financial market participants rely on existing governance and oversight arrangements for the use of AI techniques, as AI-based algorithms are not considered to be fundamentally different from conventional ones (IOSCO, 2020[39]). Model governance best practices have been adopted by financial firms since the emergence of traditional statistical models for credit and other consumer finance decisions. Documentation and audit trails are also held around deployment decisions, design, and production processes. The deployment of AI techniques in finance can generate efficiencies by reducing friction costs (e.g. commissions and fees related to transaction execution) and improving productivity levels, which in turn leads to higher profitability. In particular, the use of automation and technology-enabled cost reduction allows for capacity reallocation, spending effectiveness and improved transparency in decision-making.
- Generative AI, a technology once confined to the realms of imagination, is now opening new horizons, igniting a revolution that’s transforming the industry.
- AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets.
- 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.
- 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.
- 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.
An f5 case study provides an overview of how one bank used its solutions to enhance security and resilience, while mitigating key cybersecurity threats. The company’s applications also helped increase automation, accelerate private clouds and secure critical data at scale while lowering TCO and futureproofing its application infrastructure. Unlike automation software that can do simple, rote tasks, artificial intelligence performs tasks that historically could only be handled by humans. This positions artificial intelligence as more of a co-worker than other technologies. But despite AI’s capabilities, finance has unique responsibilities — such as validating the integrity of financial statements — that can’t be delegated to an algorithm. AI technology offers increased accuracy and efficiency and opens the doors to enormous potential and versatility within wealth management processes.
Explore the Financial services collection
Appropriate training of ML models is fundamental for their performance, and the datasets used for that purpose need to be large enough to capture non-linear relationships and tail events in the data. This, however, is hard to achieve in practice, given that tail events are rare and the dataset may not be robust enough for optimal outcomes. 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 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 Policy Guidance supports the development of core competencies on digital financial literacy to build trust and promote a safe use of digital financial services, protect consumers from digital crime and misselling, and support those at risk of over-reliance on digital credit.
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]). In theory, using AI in smart contracts could further enhance their automation, by increasing their autonomy and allowing the underlying code to be dynamically adjusted according to market conditions.
AI could also be used to improve the functioning of third party off-chain nodes, such as so-called ‘Oracles’10, nodes feeding external data into the network. The use of Oracles in DLT networks carries the risk of erroneous or inadequate data feeds into the network by underperforming or malicious third-party off-chain nodes (OECD, 2020[25]). As the responsibility of data curation shifts from third party nodes to independent, automated AI-powered systems that are more difficult to manipulate, the robustness of information recording and sharing could be strengthened. In a hypothetical scenario, the use of AI could further increase disintermediation by bringing AI inference directly on-chain, which would render Oracles redundant.
Financial consumer protection
According to a survey conducted by Irish-American professional services company Accenture, 75% of consumers are more likely to do business with a bank that offers personalized services. What’s more, according to another survey, 73% of consumers are willing to share their personal data with banks in exchange for customized offers. Rob is a principal with Deloitte Consulting LLP leading the Operating Model Transformation market offering for Operations Transformation. He also leads Deloitte’s COO Executive Accelerator program, designing and providing services geared specifically for the COO. He serves at the forefront of insurance industry disruption by helping clients with digital innovation, operating model design, core business and IT transformation, and intelligent automation.
Appendix: The AI technology portfolio12
From our survey, it was no surprise to see that most respondents, across all segments, acquired AI through enterprise software that embedded intelligent capabilities (figure 9). With existing vendor relationships and technology platforms already in use, this is likely the easiest option for most companies to choose. Frontrunners have taken an early lead in realizing better business outcomes (figure 8), especially in achieving revenue enhancement goals, including creating new products and pursuing new markets. With generative AI, companies like AppZen are automating routine tasks and transforming financial work.
Fraud detection and risk management
While many financial services companies agree that AI could be critical for building a successful competitive advantage, the difference in the number of respondents in the three clusters that acknowledged the critical strategic importance of AI is quite telling (figure 3). Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. Today, we work closely with clients to embrace a transformational approach aimed at benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and drive positive societal impact.
Artificial Intelligence in Finance [15 Examples]
Some experts even believe it could boost the world’s economy by 7% and make people 1.5% more productive. CEOs who take the lead in implementing Responsible AI can better manage the technology’s many risks. CFOs cannot afford to stand on the sidelines as generative AI reshapes the finance function of the future and its partner functions, such as marketing and HR. Embracing this technology is crucial to maintaining a cutting-edge finance organization. The use of the term AI in this note includes AI and its applications through ML models and the use of big data. Reinforcement learning involves the learning of the algorithm through interaction and feedback.
The business leaders within the institution reiterate the edge of AI algorithms over traditional models, offering an unmatched level of sophistication. AI effectively manages combating fraudulent activities, which helps to secure customers and builds trust. With the visible benefits, there are several financial services organizations that are exploring AI-based fraud prevention. Intelligent automation has the capacity to transform financial services organizations and enhance customer interactions.