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The need for AI and machine learning in volatile markets



AI and machine learning

Francesca CCO at Axyon AI


Since the outset of the pandemic, the global economy has experienced much uncertainty. Although many people expected this year to be another good year for the stock market as the world economy continues recovering from the crisis, fears over the recent developments of the Omicron variant, together with unclear messages from central banks, have caused more instability. This market volatility presents fund managers with significant operational and performance-related challenges and a potential retention issue in the face of further volatility due to new variants and unexpected developments. So, how can AI and machine learning help investors prepare for this volatility and demonstrate resilience to their clients?


AI’s transformative role for asset management firms

Ultimately, AI and machine learning enable fund and asset managers to gain valuable time to adjust risk and protect investments with the intrinsic value of predictive analytics. It can also help businesses navigate challenging conditions by detecting anomalies in the market before any crisis occurs. By implementing AI, fund and asset managers can also monetise data and improve automation from the front to the back office.


AI vs. traditional models

While nobody can predict the unpredictable, market disruption is often on the cards. Many investors have lost confidence in asset managers who managed portfolios using traditional quantitative models, as they struggled to keep up with volatile market conditions. As such, funds need to find a way to better mitigate the risks with powerful predictive analytics of fast-moving markets and avoid losing investor confidence. In today’s world, relying on traditional portfolio management models when a market crisis occurs can result in the investments becoming severely impaired and can push a large amount of chaotic data into quantitative models. Therefore, asset managers need a system that can account for volatility and manage expectations more accurately.

Traditional portfolio models are built around strong assumptions on the behaviour of underlying assets, measuring normal distribution patterns on linear scales. As a result, they find it difficult to cope with the flood of chaotic data into their systems caused by high levels of volatility and fund managers’ ability to accurately analyse and predict where the market would go next and navigate through the crisis was significantly limited. Throughout the COVID-19 pandemic and consequent volatility, businesses with these models have had their limitations exposed.

Advanced AI systems are unrivalled from portfolio management to risk management for example detecting anomalies in the market. AI models can handle large and chaotic sets of data learning from the past the actual relationship among variables. Moreover, the application of advanced analytics to these data sets may also provide more real-time insight into the risks related to these shocks for the stock market.

Unlike these traditional models, AI systems are completely agnostic about markets and their associated risks, meaning that they can be trained to sound the alarm when the structure in the data is anomalous, and therefore could be a sign of an upcoming unpredictable event. These AI-powered tools can be also used to read the reality of the situation at hand, without any pre-ordered rules.

By strengthening a model against chaotic data, AI allows fund managers to see non-linear, complex patterns in asset behaviours that can be captured, and make market view predictions at a higher level, no matter how changeable conditions become.


Turning Point: The pandemic as an opportunity for change

The pandemic is still an opportunity for new technologies to prove their merits and show that AI and machine learning can offer a better way to use data and quickly adapt to the ever-changing ‘new normal’. By modelling the potential repercussions of major geopolitical, financial, or environmental events, businesses will be better placed to adapt, reposition, and overcome the obstacles the pandemic presents.

We have seen that investment in technology and data infrastructure is working its way up asset managers’ agendas, and AI and machine learning has by no means reached its limits. Advancements in technology will mean improvements to business performance will continue, and due to the wealth of data already stored in most financial institutions, there is great potential to build on the success of previous solutions. Businesses who are late to harness the superior analytical power of AI will likely find themselves trailing behind the competition. Implementing these innovative machine learning technologies will undoubtedly be a powerful solution to the problem of meeting and exceeding investors’ expectations of mitigated risk and higher returns.



Where is the value in generative AI for financial services?




AI and machine learning

Michael Conway, Executive Partner, Data, AI and Technology Transformation Service Line Leader at IBM Consulting


The New York Times recently suggested generative AI has reached a tipping point. According to the newspaper, it’s having a “Netscape moment” – the instant where a technology triggers wide-spread, irrevocable change. Back in the ‘90s the Netscape browser unleashed the nascent power of the internet. Today, generative AI applications that can instantly produce natural language or even computer code are creating a similarly epoch-defining moment.

While it has been the consumer-focused applications of generative AI that have driven sensational headlines and captured mainstream attention, the underlying capabilities have caused businesses to sit up and pay attention. Recent IBM research found that 64% of CEOs face significant pressure from investors, creditors, and lenders to accelerate adoption of generative AI.

The banking sector has a reputation for being on the front foot with technology, but many institutions remain underprepared or unsure about how to profit from generative AI. In tandem, commentators are now talking about us reaching ‘peak generative AI’, adding to the confusion facing leaders. This risks undermining the potential benefits the technology has to offer.

Success in the long-term depends on experimentation and iteration. Here are three fundamentals that businesses can focus on now that will place them among the early winners in the generative AI era.

Michael Conway

Start with the customer experience

Today, every product is a digital product — and every company is selling a digital experience. The increasing demand for a seamless, personalised experience is driving steep competition, but businesses that can tap into the power of generative AI will leap miles ahead of their peers.

For example, a bank could use generative AI to rapidly analyse their own customer data—as well as data from social sources and partner organisations to determine which customers are most likely to take certain actions, such as opening a new account, investing assets, or applying for a loan. The AI system can then help bankers achieve true one-to-one marketing with a personalised strategy and automated, point-in-time customised offers, translated into the customer’s preferred language.

Financial services businesses can also leverage generative AI to shift digital customer service interactions from the customer needing to ask the right question, to the virtual assistant making the right suggestions intuitively. It could ‘remember’ previous conversations with the customer and know which products and services the customer is using, allowing it to provide smarter, more helpful advice. When combined with more human-like language skills, this deeper level of service will help financial institutions to build better, longer-term relationships with customers.

Supporting and upskilling employees

Looking beyond chat bots, AI can also add more value for customer service professionals. With AI and automation tools taking care of the more repetitive, mundane tasks, teams will have more time to work with customers on more complex needs and situations that call for more of a human touch. The businesses that excel in using AI and automation to augment their workforce are likely to have a sizeable competitive advantage.

To be successful, companies must be prepared to invest appropriately in upskilling colleagues so that they can work with the latest AI tools. Working with partners that can bring the right AI transformation expertise can also help businesses to bridge their skills gaps in the more immediate term. At IBM, we’ve set up the Centre of Excellence for Generative AI within IBM Consulting to help clients move forward quickly with putting this capability to work in their business.

Invest wisely in the right AI platform and expertise

It’s important to underline here that, when it comes to business use cases, we’re not talking about any old generative AI tools. While consumer applications can get away with producing incorrect or even offensive output, financial institutions have no such room for error. Customers of banks need accurate, reliable information, delivered in a professional manner that’s consistent with the bank’s overall brand experience. And that’s before you get into the requirements of financial regulators.

Using an AI platform designed for the needs of enterprises in highly regulated industries is therefore a must in financial services. That means the AI models being used are comprised of data that has been screened for things like bias or harmful content and which can be traced to its source. It means the data and the AI models the institution is using have governance controls baked in, so that the outputs are explainable and transparent.

Financial institutions also need AI models that are tailored for the specific domain areas of their business and that are interoperable across different cloud environments, which is important to regulators like the FCA. As IBM AI is built for businesses, we have built all of these requirements into our watsonx AI and data platform for the enterpise.

Commercial value beyond the hype

Are we in a generative AI hype cycle? Yes. But don’t be fooled. This technology is already starting to transform financial services – and virtually every other industry. Those who can harness it effectively stand to reap immense benefits – from more satisfied customers to lower costs, greater productivity and faster innovation.

Don’t wait for the perfect conditions, they’ll never come. Start now, start small, then scale your generative AI applications across the business. Focus on the use cases where you can gain early commercial value – such as customer experience and automating repetitive tasks – and work with technology designed for the enterprise. In a couple of years, you’ll be very glad you did.

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Connecting the security dots with cyber fusion 




Anuj Goel, Co-founder and CEO at Cyware 

Against the backdrop of Russian-based hacktivists declaring war on Europe’s financial systems, the passing of the EU’s Digital Operational Resiliency Act (Dora) and the potential threats posed by the emergence of generative AI, the finance sector has a lot to contend with. 

In today’s elevated threat environment, cybersecurity teams need to take proactive action fast. All too often, however, analysts are bombarded by a tsunami of alerts generated by countless security tools. According to recent estimates, today’s enterprises have on average 100+ discreet security tools, many of which do not play nicely together.  

At the same time as attempting to make sense of all this noise, IT security teams and their risk counterparts often work in isolation and rarely share resources or intel. Consequently, both teams are on the lookout for external indicators of looming threats despite the fact that internal log data often contains clues to the next attack. Without the right tools to effectively process and analyse this vast sea of data, these clues stay undetected, only to be discovered forensically long after an attack occurs. But rather than simply adding more security tools into the mix, security professionals need a better way to examine the threat data generated by disparate security tools and deduce high confidence and actionable threat intelligence. 

To improve their threat detection and response capabilities, banks need to adopt a cyber fusion strategy that makes it easier and faster to find indicators of potential compromise and collectively take informed defensive steps to prevent or mitigate an incident. 

What is cyber fusion? 

Initially developed by intelligence agencies to promote collaboration through intelligence sharing, the fusion centre concept is now gaining traction in the field of cybersecurity. 

Unifying security functions such as threat intelligence, security automation, threat response, security orchestration and incident response into a single connected unit, cyber fusion offers a more proactive approach to dealing with potential threats by bridging the gap between multiple teams through intelligence synthesis and inter-team collaboration. It also enables the fusion of contextualised strategic, tactical and operational threat intelligence for rapid threat prediction, detection and incident response.  

By initiating a cyber fusion centre (CFC), banks will be able to automate the ingestion of threat data from a variety of different sources including existing security tools, cloud apps, historic incident intelligence and other data sources, including external threat intelligence providers and regulatory advisories. This can be done in a way that allows security teams to contextualise insights into malicious activities and meaningfully orchestrate cybersecurity operations across the network. 

Leveraging AI and machine learning to enable faster actioning and analysis of threat intelligence, a CFC delivers complete visibility of security risks, threats, security controls and exceptions across cloud-based or on-premises infrastructures. It also enables banks to automate incident response and respond to threats in real time or proactively.  

Finally, and most importantly, it also boosts inter-team collaboration by automatically notifying the right stakeholders of relevant threat intelligence and changing scenarios in real-time via a shared platform that supports a truly holistic and joined-up response. 

Enabling a unified security posture 

Bringing together technologies, teams and processes under one roof, a CFC enables security teams to orchestrate and automate security workflows in an integrated and highly collaborative manner. 

Providing insights on all kinds of threats including malware, vulnerabilities, threat actors and previous incidents, cyber fusion supports the rapid dissemination of intelligence among all security teams to enable high-fidelity security decision-making at a technical, tactical, operational and strategic level. The exchange of situational intelligence at a cross-sectoral level empowers security teams to co-develop threat mitigation strategies. It also enables teams to leverage shared actionable intelligence to automate responses – such as blocking malicious IPs in firewalls or updating SIEM data – with no need for manual intervention. 

But that’s not the only benefit. To further reduce security vulnerability risks, banks can use their CFC platform to automatically feed relevant data into their other security tools (EDR, firewalls, IDS/IPS, SIEM, SOAR). Using automated cross-functional workflows to drive security actions significantly reduces the mean time to detect (MTTD) and mean time to respond (MTTR). 

Connecting the dots for enhanced resilience 

With a cyber fusion centre in play, banks can enable security teams to ingest, enrich, correlate and manage threat data into a single source of truth and turn that data into contextualised, noise-free and actionable threat intelligence. This can also then be shared in real time to identify and respond to threats faster. 

Enabling 360-degree threat visibility is just the start. Alongside promoting collaboration between teams by sharing real time threat alerts that support a collective defence approach, a CFC enables security operations teams to automate incident responses and initiate an end-to-end threat response process that keeps pace with the evolving threat landscape. By adding cyber fusion capabilities to their existing security operations centre (SOC), banks will be better equipped to connect the dots and respond to the prevailing threat landscape in real time. 

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