Finance teams are now using AI to outperform peers
Unit4, a leader in enterprise cloud applications for people-centric organisations, has released the findings of Finance, AI and the future of decision making, Unit4's global survey of finance professionals.
The survey found that while awareness of AI among finance professionals is high with 70% at least with a little knowledge about AI, it was clear that those embracing it are outperforming their peers. When contrasted with respondents who have not yet adopted AI there are clear differences:
- 47% of those who adopted AI believed their companies were performing strongly, compared to only 28% of non-adopters.
- 49% of AI adopters felt their companies had strong leadership in contrast to only 32% of non-adopters.
- Looking ahead 43% of AI adopters compared to only 21% of non-adopters believed they will maintain their strong position in 12 months.
"AI is very much on the mind of finance departments around the world with early adopters confident it is having a positive impact on the performance of their organisations," said Gordon Stuart, CFO, Unit4. "However, AI should not just be seen as a way to automate processes. Finance professionals should use it as an opportunity to move into a more strategic role, becoming storytellers and influencers, interpreting data through technology analysis, and translating the findings into meaningful insights relevant to their organisations."
In the next two years, 83% of respondents expect to be increasingly involved in strategy and decision making, while 75% say their day jobs will have changed significantly in the same timeframe. There is a very strong desire to upskill, as 83% say that those in finance roles will have to enhance their skillsets in the next two years.
In the next 12 months, more than four-fifths of respondents are expecting to focus this upskilling on AI, machine learning, coding, analytics, and data science capabilities, but a third of respondents accept their organisations will need to bring in new hires to address the skills gap.
The value of AI in finance
Respondents to Unit4's study suggest the top benefits of AI are improving data quality (33%) and saving time (32%), but only a quarter of respondents say it would help colleagues make faster decisions, and only 24% believe it would deliver actionable insights for decision making.
"From our experience, if finance professionals want to play a more strategic role they must have the business and leadership skills to identify what the business needs to prioritise to succeed," Georgina Kossivas, Chief Financial and Risk Officer, Nuclear Waste Management Organisation. "Communications skills are also incredibly important to articulate insights in terms that are relevant to senior decision makers. Investments in AI will only deliver on their true potential if finance professionals have developed these skills sufficiently."
Reimagining operational risk management for business value
The events of 2020 and 2021 have fundamentally changed how we do business, upending every industry, including investment banking. Once bustling trading floors went silent as the switch to work from home led traders to disperse locations – and gave rise to new operational risk challenges.
Today’s dynamic regulatory landscape coupled with ongoing technological innovations have made legacy approaches to operational risk management ill-suited to tackle current challenges and complexity. And while many financial institutions have turned to digital automation and transformation projects to adapt traditional ‘revenue generating’ functions to meet their challenges and help drive growth, they must now do the same with their Operational Risk Management (ORM) functions - or risk being left out in the cold.
The Basel Committee defines operational risk as the “risk of loss resulting from inadequate or failed internal processes, people and systems or from external events.” Unfortunately, many financial institutions still view ORM as a regulatory and compliance necessity rather than a business function that delivers real value. That means executives and risk management departments must now change their risk approach to ensure they are dynamic and flexible, can guide their organizations through complex situations, and can readily meet the evolving expectations of regulators and their clients.
Operational Risk Management is still a young field compared to other risk sectors in the financial markets, but it has always been viewed under a broad umbrella that encompasses risks and uncertainties difficult to quantify and manage in traditional manners. ORM has also been the convergence point where corporate governance issues overlap with revenue-generating business activities, causing potential confusion between departments.
Investment banks have too often placed undue emphasis on creating governance frameworks designed to ensure they meet Basel Committee on Banking Supervision (BCBS) standards instead of recognizing that a sophisticated ORM function can bring quantifiable value. Their desire to merely meet BCBS standards and avoid historic risks has in effect led to an outdated, analogue approach in an increasingly digital world. Savvy investment banks have grasped the value potential of ORM and begun to drive a shift in awareness about the importance of a comprehensive risk identification, measurement, and mitigation program.
Embracing a data-driven approach
Market players now recognize that adopting a digital strategy will allow them to deploy diverse and agile risk management mechanisms. It will also empower them to develop a strong and dynamic understanding of risks while adding real value to the business. This value goes beyond meeting regulatory and compliance mandates introduced as part of the Standardized Measurement Approach developed under Basel 3. A robust approach to risk allows the ORM functions to provide actionable intelligence to support business decision-making and assume a more commercial role that supports the various business units’ day-to-day activities. And that requires an intelligent, data-driven approach with a mandate to match, one that is championed at all levels of the organization.
This type of aggressive approach and embrace of digital transformation can also strengthen how ORM functions handle ambiguous and/or improbable events, especially as traditional methods of risk analysis prove unable to manage the ever-increasing volume of data. In 2010, the total amount of data created, captured, copied and consumed equaled about two zettabytes, compared to 2018 when volumes reached about 33 zettabytes. This 26% compounded annual growth rate means that if the rate of growth steadily continues by 2024, we can expect 149 zettabytes of data created per annum.
Available data levels will make it difficult for analogue ORM functions to successfully meet the executive expectations, however organizations that adopt a data-driven approach will find increased data volumes provide them the insights to gain a competitive advantage and ability to proactively manage their risk.
Leveraging AI and advanced analytics for high impact
Cognitive computing technologies like artificial intelligence (AI), data mining and natural language processing (NLP) can supplement a data-driven approach and help financial institutions confidently automate decisions, optimize processes and provide a deeper insight into available data. These cognitive computing technologies can help reduce or eliminate time-intensive and repetitive tasks, often related to data collection, handling and analysis which are better suited to automation. That in turn can free up critical employees to deploy their experience, knowledge of policies, and powers of assessment to support ORM functions and achieve their goals and focus on high-impact, high-value deliverables.
Cognitive computing can teach computers to recognise and identify risk, which is especially useful to handle and evaluate unstructured data – the kind of data that doesn’t fit neatly into structured rows and columns on a spreadsheet. Natural language processing (NLP) can analyze text to derive insights and sentiments from unstructured data, which a 2015 study by the International Data Group estimates accounts for 90% of all data generated daily. When combined with the estimated future data volumes, cognitive computing functionality presents an immense opportunity for ORM functions to add additional business value in ways previously impossible. A detection model built on cognitive analytics can manage risk on a near real-time basis and can also unlock organizations’ historic datasets that have been compiled for internal, regulatory, or compliance purposes. These datasets often contain free text descriptions that contain a potential wealth of untapped, institution-specific information and could provide valuable insight into historic operational risk losses, providing data to augment employee’s qualitative experiences.
Teaching an old dog new tricks
There are certainly challenges to launching digital transformation projects, implementing new data-driven approaches, and introducing cognitive computing technologies, including employee uncertainty and ethical considerations. That means financial institutions must preemptively address and prepare for potential challenges before they adopt a technology-enabled approach to Operational Risk Management. They must also secure employee buy-in to ensure stakeholders use these new technologies to their full potential and to assuage any concerns that technology diminishes employees’ important role in the organization.
It’s critical that investment banks now shift their Operational Risk Management functions and focus on becoming more adaptive and agile in an increasingly volatile, complex, and uncertain world. Over 66% of banking executives report that adopting new technologies like AI and NLP will be a key driver in IBs development through to 2025. Yet for many investment banks, their ORM functions do not leverage the powerful new tools available to them – including increased computing power, digitization, advanced analytics, and data visualization techniques – much less harness the power of cognitive computing technologies. Until ORM functions leverage these tools, executive leadership cannot allocate resources and solidify ORM’s role in business strategy, performance, and decision-making processes.
Old habits die hard, but it’s time for ORM functions to keep pace with these new technologies, methodologies, and approaches to position themselves and their organizations for success in today’s ever-changing world. If they do not adapt, there is a real risk they may stifle the wider organization, impede new opportunities and inhibit paths to valuable business growth.