Technology is changing the asset management industry as we know it. Early adopters have the chance to outpace the competition. Oliver Kroll and Victor Chicha of AI-based credit risk assessment company Scorable discuss asset management and the rise of AI.
Global assets under management have grown considerably over the last decade, currently amounting to around $80trn. Despite this, asset managers have not had an easy ride. Fierce competition, increasingly complex regulation and record low interest rates are putting pressure on the industry.
At a time, when passive funds regularly outperform actively managed funds, asset managers need to prove their value to investors.
Asset managers who want to stay ahead of the game need to boost efficiencies, improve analytics and speed up processes by adopting more innovative technologies. Research suggests that in the past three years, digitally mature organizations have delivered 25% higher revenue and 31% higher Ebitda.
Oliver Kroll, chief product officer and Victor Chicha, product evangelist at AI-based credit risk assessment company Scorable say: "A major challenge for asset managers is the ever growing amount of data they have to process to stay competitive and to deal with increasingly strict regulatory requirements for internal credit risk assessments.
"In the last two years, for instance, we have seen nine times more data created than in the entire history of humanity. In theory, more data should deliver more precise analytical results. However, many fixed income managers still rely heavily on manually driven procedures: incorporating additional data into their analysis can put a huge strain on their capacities and is often not supported by traditional systems."
Challenges require sophisticated tech
To deal with today's challenges the asset management industry needs more efficient risk assessment solutions. This is why Scorable has developed an innovative artificial intelligence (AI) solution to help fixed income managers make more informed investment decisions ahead of the market.
"Our technology enables them to monitor corporate bonds and to anticipate rating downgrades before they happen or the markets price them in," Chicha explains.
"By leveraging our AI, investment professionals can incorporate more information into their analysis with greater efficiency, and thus cover a greater number of issuers.
"What makes Scorable special is that our AI system goes beyond the usual quantitative data and incorporates qualitative data into the risk assessment with the help of natural language processing (NLP) and machine learning. Thus, our system can process much greater amounts of relevant data compared to traditional credit risk analysis."
Chicha continues: "Our data sources include, for instance, issuer fundamental ratios, market data, credit ratings, industry data, and financial news. Based on this data, Scorable models the likelihood that the company's creditworthiness will deteriorate within the next twelve months.
"By combining and contextualising quantitative and qualitative data, we can more accurately calculate the risk to fixed income securities and achieve more comprehensive insights."
Move ahead of the market
With Scorable's AI, investors are able to detect changes in the credit risk of corporate bonds early and to act ahead of the market. This is not only essential to avoid the risk of default, it is also crucial when, for example, a corporate issuer gets downgraded from investment grade to high yield.
"Portfolio managers in charge of rating-based investment mandates are then forced to sell their position within a couple of weeks, resulting in a high supply and often a drop in price," adds Kroll. "By detecting a change in credit risk early, portfolio managers may be able to move ahead of the market, thus avoiding fire sales and protecting the value of their portfolio."
Scorable currently covers more than 80% of outstanding corporate bond debt in major currencies. "We constantly update and enhance our technology to respond to our users' needs and to stay on top of market developments," states Kroll.
"One of our latest additions is our relative value feature which enables investors to see which bonds are cheap or expensive compared to the rating class they are in. Moreover, we continuously expand our data sources to further enhance our scoring models."
AI increases transparency
Whilst the use of AI can bring huge benefits, the topic is not without controversy. Bias, accountability and explainability are some of key concerns that financial regulators have cited when it comes to AI. It is fair to say that the lack of transparency and the prevalence of black-box models have certainly been a key challenge to AI adoption across the industry.
Chicha comments: "At Scorable, we are conscious that portfolio managers and analysts cannot simply take findings from a black box model and include them into their investment processes.
"Very often, output from AI models is not even intuitive. This is why we have developed an explainable AI approach which allows users to intuitively understand the rationale behind the analysis and to see what factors drove changes in the risk score.
"Unlike black-box models which only show the input output relationship, our models provide understandable features and a transparent machine learning process. Thus, users can clearly see, for instance, why the model has determined that a rating would be downgraded."
There is no denying that the asset management industry is undergoing a fundamental change. AI has been finding its way into the financial industry and asset management for some time. The need to become more efficient in light of increasing competition, regulation and pressure on fees will further drive and accelerate technological progress.
AI can provide significant benefits to the asset management industry by boosting efficiencies, cutting repetitive processes, increasing accuracy and avoiding human error, and reducing costs.
When it comes to interpreting the ever-growing amount of data, artificial intelligence can offer real added value in decision-making. Scorable already sees many user cases that can no longer be covered by existing systems, but require more sophisticated technologies to handle the exponential growth in data volumes.
Early adopters of technology will certainly be able to gain competitive advantages. It is not a question about man versus machine but a question of how active asset managers can use the right technology to generate alpha and outperform their competition.
While AI still has some obvious weaknesses - particularly in relation to its black box nature - the digitisation of the asset management industry is unstoppable.
Kroll concludes: "We believe that in the coming years explainable AI will be a major game changer as it can help overcome many of the hurdles that currently prevent its adoption and increase transparency."