Council Post: Artificial Intelligence In The African Asset Management Industry: Four Factors To Consider Before Developing A Strategy

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author.

Subomi Plumptre is a licensed fund manager & co-founder at Volition Cap, where she champions middle class wealth for Africans and Diasporans

Bayo, a young associate in an African consulting firm, read about a new artificial intelligence (AI) platform called ChatGPT. He wondered if the viral invention could solve his current predicament. At 32 years old, Bayo had worked for a decade without concrete savings. Lacking financial literacy, he wanted an investment plan that he could easily implement and asked ChatGPT first. The response Bayo got was interesting.

Bayo's use of ChatGPT speaks to the ways in which users are interacting with artificial intelligence, including asking it for financial advice.

According to Harvard University, the field of artificial intelligence really took off in the '80s. Unlike regular computer software, it can imitate some features of intelligent behavior, bringing a human-like persona to programming enquiries. AI gets better as people use it and then rate the quality of responses they receive. This phenomenon is called machine learning (ML).

Discussions about AI's potential to disrupt enterprises, business models and entire industries have become mainstream. Security on a global scale is also an important area of disruption. For example, in 2020, the Kargu-2, an autonomous attack drone that allegedly uses machine learning, made its debut.

As AI expands its reach into various industries, I believe the financial services sector may not be left out as it reshapes asset management.

Africa has witnessed an enthusiastic uptake of AI in the broader financial services industry, mostly for banking front-end processes like customer authentication and virtual assistance. Chatbots have become quite common with the likes of South Africa's Nedbank and Nigeria's Zenith Bank launching their Enbi chatbot assistant and ZiVA (Zenith Intelligent Virtual Assistant), respectively, in 2022 and 2021.

In 2021, the African Development Bank (AfDB) provided $1 million in grants for AI-based national customer management systems in Ghana, Rwanda and Zambia.

In the asset management sub-sector, key AI use cases now include investment research, portfolio management and investment advisory. Investment advice is suggested by AI, and then reviewed by a licensed fund manager before it gets to the customer. But, as the use of AI scales across financial services institutions, companies may potentially eliminate the middleman reviewer. A parallel exists in highly specialized fields like medicine, where researchers use AI to crunch large data sets to detect diseases. Will doctors soon no longer play a role in diagnosis?

As an African asset manager considering your AI strategy, here are four things to bear in mind.

1. Factor in the competition from boutique asset management companies.

With the advanced data processing speed that AI affords, smaller asset management firms can now onboard clients faster using AI enabled know-your-customer (KYC) software. They can deploy "robo-advisors" to craft bespoke investment advisory solutions at low cost. Cloud-based services have made it possible to roll out these solutions at scale by replacing expensive on-premise infrastructure.

If you are interested in implementing AI-powered KYC software, look into which application program interfaces (APIs) from a third-party commercial vendor would suit your business best. These APIs can be layered onto existing company websites to collect and vet customer identity or residential information.

2. Improve your data sets.

A common characteristic of AI and ML is the capacity for improvement. In my experience, asset management companies tend to keep rigorous customer data, thanks to regulatory requirements. This data may allow sub-Saharan companies to shore up some of the deficiencies of current AI platforms -- the paucity of African data sets and the inherent bias towards the Black demographic. Whoever becomes the repository of this financial data set would be in an enviable position.

There are a few things companies can do to build significant financial data sets that can be used for AI applications. The first step is to ensure that the data privacy policy of the organization allows for the use of anonymized data. The next is to integrate AI analytics software that can sort, categorize, analyze and then store customer data.

3. Tackle AI's inherent bias.

AI does not only mimic human intelligence, but it reproduces biases as well. At the moment, Western data mostly power AI and so, there is an urgent need to fine-tune the technology to fit African socio-cultural contexts. The asset management industry has the financial muscle to fund the research and advocacy to even out these biases. In being part of this, you can help provide a more enriching customer experience.

For business leaders who wish to fund African AI data sets, I would suggest doing so through independent nonprofits, universities or partnerships with African multilateral institutions, such as the African Union, to make the data open source.

4. Consider implementing gamification and tokenization to your AI strategy.

The gamification of finance using robo-advisors and tokenization is causing many newbie investors to see investing as something that can become fun, rather than a subject to be feared. This changing attitude can play to the advantage of asset managers who adopt AI to build financial gamification platforms.

Some best practices for gamification include adding a behavior scientist to product development teams ab initio, to design the user experience. Aim for creating an addictive game, as this can lead to increased customer loyalty.

It appears that no matter how you look at it, AI is a double-edged sword in terms of its capacity to both disrupt and enable the African asset management space. But I believe fund managers and the asset management companies that employ them will still be relevant for a while to come.

As co-founder of Collibra, Stijn (Stan) Christiaens said, "If I can only give one answer to the question who will be the last to be disrupted by AI, it would be Strategic Consulting." If Mr. Christiaens is right, then to the extent that the fund manager acts as a strategic financial consultant to clients, and does so in a highly regulated industry, they may be among the last set of professionals to be disrupted by AI.

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