Deep Impact: The Emergence Of AI-Driven Processes In Finance

Across a diverse set of areas, 64% of finance organizations using AI report that its impact has either met or exceeded their expectations. These CFOs can expect this impact to compound as their more complex AI techniques mature and provide greater value in Year 2 or 3. To attract this key talent, AI-forward CFOs adjust their recruitment strategies, develop new career paths and invest in data science technologies and development opportunities for current staff. These CFOs also adjust their hiring focus to create talent pipelines and develop trainings for candidates with nontraditional finance backgrounds.

The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack. The results can not only inform the finance team with better, faster information, it can influence the strategic thinking of the entire organization. AI can help reduce financial crime by detecting sophisticated fraud and detecting aberrant behavior as corporate accountants, researchers, treasurers, and financiers strive for long-term success. Companies must also engage with stakeholders in an ongoing, proactive effort to shape the narrative around performance and prevent misinterpretations that might arise from AI-generated analysis.

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

AI is particularly helpful in corporate finance as it can better predict and assess loan risks. For companies looking to increase their value, AI technologies such as machine learning can help improve loan underwriting and reduce financial risk. AI can also lessen financial crime through advanced fraud detection and spot anomalous activity as company accountants, analysts, treasurers, and investors work toward long-term growth. Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry.

Figure Marketplace uses blockchain to host a platform for investors, startups and private companies to raise capital, manage equity and trade shares. AI assistants, such as chatbots, use AI to generate personalized financial advice and natural language processing to provide instant, self-help customer service. Socure created ID+ Platform, an identity verification system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately. Socure is used by institutions like Capital One, Chime and Wells Fargo, according to its website. Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history.

What the Finance Industry Tells Us About the Future of AI

According to Towards Data Science, AI can analyze prospective buyers more quickly and correctly based on a range of characteristics, including smartphone statistics. Fraud detection systems use AI to examine a person’s purchasing habits and raise an alarm if something appears out of the ordinary or contradicts your usual spending habits. Taking a proactive stance will not only strengthen the company against heightened scrutiny but also position it to thrive and be resilient amid global market volatility. A looming menace for numerous companies is the rise of insidious deepfakes, an ominous frontier that presents grave threats to corporate earnings by potentially manipulating disclosures.

This work highlights the important role technology can play in helping countries make well-informed decisions and achieve more efficient financial outlays, by mobilising private sector investment, by enhancing service delivery and by achieving environmental, social and economic benefits. 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. The use of NLP could improve the analytical reach of smart contracts that are linked to traditional contracts, legislation and court decisions, going even further in analysing the intent of the parties involved (The Technolawgist, 2020[28]). It should be noted, however, that such applications of AI for smart contracts are purely theoretical at this stage and remain to be tested in real-life examples.

The opacity of algorithm-based systems could be addressed through transparency requirements, ensuring that clear information is provided as to the AI system’s capabilities and limitations (European Commission, 2020[43]). Separate disclosure should inform consumers about the use of AI system in the delivery of a product and their interaction with an AI system instead of a human being (e.g. robo-advisors), to allow customers to make conscious choices among competing products. 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. Operational challenges relating to compatibility and interoperability of conventional infrastructure with DLT-based one and AI technologies remain to be resolved for such applications to come to life.

Over-reliance on outsourcing may also give rise to increased risk of disruption of service with potential systemic impact in the markets. Similar to other types of models, contingency and security plans need to be in place, as needed (in particular related to whether the model is critical or not), to allow business to function as usual if any vulnerability materialises. Possible risks of concentration of certain third-party providers may rise in terms of data collection and management (e.g. dataset providers) or in the area of technology (e.g. third party model providers) and infrastructure (e.g. cloud providers) provision. AI models and techniques are being commoditised through cloud adoption, and the risk of dependency on providers of outsourced solutions raises new challenges for competitive dynamics and potential oligopolistic market structures in such services. The quality of the data used by AI models is fundamental to their appropriate functioning, however, when it comes to big data, there is some uncertainty around of the level of truthfulness, or veracity, of big data (IBM, 2020[31]). Correct labelling and structuring of big data is another pre-requisite for ML models to be able to successfully identify what a signal is, distinguish signal from noise and recognise patterns in data (S&P, 2019[19]).

Artificial Intelligence in Personal Finance

AI-supported processes must support a transparency that allows people to observe the process and freely take control when necessary. Learn why digital transformation means adopting ai in finance digital-first customer, business partner and employee experiences. Learn how AI can help improve finance strategy, uplift productivity and accelerate business outcomes.

AI can help companies drive accountability transparency and meet their governance and regulatory obligations. For example, financial institutions want to be able to weed out implicit bias and uncertainty in applying the power of AI to fight money laundering and other financial crimes. Prebuilt AI solutions enable you to streamline your implementation with a ready-to-go solution for more common business problems. Oracle’s AI is embedded in Oracle Cloud ERP and does not require any additional integration or set of tools; Oracle updates its application suite quarterly to support your changing needs.

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. Clients receive 24/7 access to proven management and technology research, expert advice, benchmarks, diagnostics and more.

• Overall, the integration of AI in finance is creating a new era of data-driven decision-making, efficiency, security and customer experience in the financial sector.
• The phased entry into force also allows a year before applying rules on foundational models (aka general purpose AIs) — so not until 2025.
• Importantly, the lack of explainability makes discrimination in credit allocation even harder to find (Brookings, 2020[20]).
• The company’s cloud-based platform, Derivative Edge, features automated tasks and processes, customizable workflows and sales opportunity management.

Furthermore, larger enterprises tend to use combinations of different software tools and platforms to house their data. Often, these systems do not easily communicate with one another — leaving decision-makers in a tough spot. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists.

Data science and analytics

Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. More importantly, CFOs are ready to explore AI’s potential–“accelerated business digitization,” including AI, was one of the top strategic shifts CFOs said their companies were making in response to a turbulent economic environment brought on by the pandemic.

AI is also changing the way financial organizations engage with customers, predicting their behavior and understanding their purchase preferences. This enables more personalized interactions, faster and more accurate customer support, credit scoring refinements and innovative products and services. 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.