Listado de la etiqueta: Artificial Intelligence

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How AI is transforming the Wealth Management industry

We see a lot of articles on how Artificial Intelligence (AI) is transforming or disrupting various industries. A lot of these transformations apply to any industry or sector. So, the automation of back-office processes or using the latest LLM-built chatbots for improved communications with customers will help a bank, an energy company, or a toy manufacturer.

Occasionally these articles zoom in on sectors that we are particularly interested in like Wealth Management. A couple of interesting recent examples are:

  • Yahoo Finance article titled “AI will bring ‘carnage’ to wealth management, strategist says”. It argues that AI will disrupt the wealth management industry by enabling low-cost and high-quality robo-advisors to replace human advisors. This will force smaller companies to consolidate and spend heavily if they want to remain competitive. To back this finding, the article refers to a recent PwC survey predicting “that 1 in 6 asset and wealth management companies will be bought or shut down in the next five years”.
  • Blackrock article titled “How AI is transforming investing”. It details how large fund managers have evolved to using LLM-like Transformers to analyse text sources and generate return forecasts. It is an interesting read that provides a primer into how these quantitative portfolio allocation and management tools work and what is the state of the art for large Wealth Managers.

We don’t agree with everything that these articles say or how they are positioned. For example:

  • It may be fair to say that robo-advisors can replace humans for customers who are highly digital native and content with passive investment. However, many customers still want (and will continue to want) personalised human attention and individual portfolio construction and management.
  • While it is fascinating and impressive how well Transformers appear to predict future returns, customers still want (and will continue to want) to invest in more traditional products based on Value, Growth or passive strategies.

Scale

One thing that both articles have in common is the relevance of scale. Scale is necessary to spread the costs of robo-advisor development across more customers. Scale also enables you to have the brainpower and skills to build the best predictive models and algorithms.

This does not mean that smaller Wealth Managers are doomed to fail or be consolidated. But it does mean they should start thinking about how they can adapt to and benefit from AI.

This can be as simple as starting to use AI to do more straightforward tasks (e.g. LLM-generated job descriptions or AI-produced meeting transcripts) or using AI to augment/speed up your internal work (e.g. see our recent article on policy writing).

At the same time, smaller Wealth Managers should define which parts of the business they want to be in the longer term and how (e.g. they may decide to distribute the best third-party Quant products, but keep other portfolio management capabilities in-house). They should also continuously monitor what AI solutions are available in the market for their size and type of business (new solutions are constantly coming out, so this cannot be a one-off exercise). For example, a quick search yields various third-party tools like AlphaSense or Kensho that a small Wealth Manager could trial in order to better predict future returns (Note: we have not tested these tools ourselves, so this is not an endorsement).

How this plays out will in part depend on the quality of tools that get developed over the next few years and how well the smaller Wealth Managers adapt to the changing environment. Wealth management clients are often loyal and a lot of the demographic is unlikely to change in the short term, but Wealth Managers should always be looking ahead.

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How LLMs Can Simplify Writing Company Policies and Procedures

Policies and procedures are essential documents for any organization, as they define and guide its operations and activities. However, writing these documents can be challenging and time-consuming, especially for complex or technical topics, such as user acceptance testing (UAT). 

UAT is a type of testing that involves the end users or clients of a software system or application to verify if it meets their requirements and expectations. UAT is usually the final testing stage before the software is released or deployed, and it requires careful planning, execution, and documentation. 

In the past, companies would hire expensive specialist consultants who have the expertise and experience in this field to help them write complex policies and procedures. However, with the advancement of artificial intelligence (AI) and natural language processing (NLP), there is a new option available for companies: large language models (LLMs). 

What are LLMs? 

LLMs are a type of generative AI that can produce text based on a given input or prompt. LLMs are trained on massive amounts of text data to learn the patterns, structures, and relationships in natural language. LLMs can perform various language-related tasks, such as translations, summaries, questions, answers, sentiment analysis, and more. 

Some examples of LLMs are OpenAI’s GPT-4, Google’s BERT, and Facebook’s BART. These models have shown impressive capabilities in generating coherent, fluent, and relevant text for various purposes and domains. 

How can LLMs help with writing policies and procedures? 

LLMs can help companies with writing policies and procedures for UAT by taking some of the grunt work out of it and enabling internal staff to just edit or curate the results. Here are some of the benefits of using LLMs for this task: 

  • LLMs can save time and money by reducing the need for hiring external consultants or spending hours on research and writing. 
  • LLMs can generate text based on keywords, topics, or examples provided by the user, making it easier to customize the content according to the specific needs and preferences of the company. 
  • LLMs can produce text in a clear, concise, and consistent manner, following the best practices and formats for writing policies and procedures. 
  • LLMs can provide suggestions, alternatives, or variations for different scenarios or situations that may arise during UAT. 
  • LLMs can incorporate relevant information from various sources, such as industry standards, regulations, best practices, or previous projects. 

How to use LLMs for writing policies and procedures? 

Using LLMs for writing policies and procedures is not as simple as typing a query and getting a ready-made document. LLMs are powerful tools that require careful guidance, supervision, and evaluation by human experts. Here are some steps and tips on how to use LLMs for this purpose: 

  • Define the scope and purpose of the policy or procedure you want to write. Identify the target audience, the objectives, the criteria, and the expected outcomes. 
  • Choose an appropriate LLM that suits your needs. Consider factors such as the domain, the language, the quality, and the availability of the model. 
  • Provide a clear and specific input or prompt to the LLM. You can use keywords, topics, examples, questions, or instructions to guide the model on what you want it to generate. 
  • Review and edit the output of the LLM. Check for accuracy, relevance, completeness, and coherence of the text. Correct any errors, gaps, or inconsistencies in the content, style, tone, or structure. 
  • Validate and verify the policy or procedure with the stakeholders, such as the end users, clients, managers, or regulators. Get feedback and approval before publishing or implementing the document. 

Writing technical policies and procedures can be a daunting and tedious task, but with the help of LLMs, it can be made easier and faster. 

 

Note: we generated the above text using BingChat, which runs a version of ChatGPT. We generated the image above using BlueWillow, an AI image generator.  

Note 2: Click here for an example of an AML policy for a small UK investment manager written by an LLM 

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Can AI replace project managers? We asked ChatGPT.

Most of you by now, if you weren’t already aware of it, will have noticed in the media the buzz around ChatGPT, an AI chatbot launched in November 2022 by OpenAI. With a small amount of existential trepidation we went straight to the question that matters (to us!) most: “Can AI replace project managers?”.

While the writing style is a little formulaic, it provided an interesting breakdown of where AI could and could not currently assist in the delivery of projects, highlighting the key skills that project managers bring:

“Can AI replace project Managers?” – response generated by ChatGPT

Artificial Intelligence (AI) has the potential to revolutionize many industries, including project management. However, it is unlikely that AI can completely replace project managers in the near future. While AI can assist project managers in certain tasks such as scheduling, resource allocation, and risk management, project management involves a lot of human interaction, decision making, and problem solving, which are still difficult for AI to replicate. Additionally, project managers also play important roles in communication, motivation, and leadership, which are not easily replicated by AI.

One of the main responsibilities of project managers is scheduling, which involves creating a plan for the project, setting deadlines, and ensuring that all tasks are completed on time. AI can assist in scheduling tasks by analyzing data, identifying patterns, and making predictions about project timelines. However, AI systems lack the ability to consider all the nuances and variables that a human project manager can take into account while creating a schedule.

Another important responsibility of project managers is resource allocation, which involves determining which resources (e.g. personnel, equipment, etc.) are needed for the project, and ensuring that they are available when needed. AI can assist project managers in resource allocation by analyzing data and identifying patterns that can help predict resource usage. However, AI systems lack the ability to make judgement calls and take into account the human aspect of resource allocation, such as personal abilities and preferences.

Risk management is another important responsibility of project managers. They need to identify potential risks that could impact the project and take steps to mitigate or avoid them. AI can assist project managers in risk management by analyzing data, identifying patterns, and making predictions about potential risks. However, AI systems lack the ability to anticipate and mitigate risks that are not based on past data.

Project management involves a lot of human interaction, decision making, and problem solving, which are still difficult for AI to replicate. Project managers need to be able to communicate effectively with team members, stakeholders, and clients, adapt their communication style to different audiences and be able to explain complex technical concepts in simple terms. AI is not yet advanced enough to replicate this level of communication.

Motivation is another important aspect of project management. Project managers need to be able to motivate team members to work towards a common goal, and to overcome obstacles. They need to be able to create a positive and productive work environment. AI is not yet advanced enough to replicate this level of motivation.

Leadership is also an important aspect of project management. Project managers need to be able to lead teams and make important decisions. They need to be able to set a vision for the project and inspire others to work towards it. AI is not yet advanced enough to replicate this level of leadership.

In summary, while AI can assist project managers in certain tasks, it is unlikely to completely replace them in the near future. Project management involves a lot of human interaction, decision making and problem solving, which are still difficult for AI to replicate. Additionally, project managers also play important roles in communication, motivation, and leadership, which are not easily replicated by AI.

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Introducing DELTA AI

delta-ai.com

We attend a number of conferences and events and meet a lot of Fintech startups, working at the forefront of Artificial Intelligence, Blockchain and other technologies. This is fun and fulfilling at a personal development level but also helps us be able to think outside the box in our daily conversations with our clients. Often their challenges or objectives can be addressed relatively quickly and inexpensively using Artificial Intelligence solutions, such as Robotic Process Automation, predictive modelling based on Machine Learning or something as simple as a chatbot. This usually comes as a surprise to them as their experience of AI is as a hyperbole-filled topic oversold and under-delivered in the market to date. Most of our clients haven’t got the time to separate fact from fiction so we do a lot of educating in simple language about AI and what is and what isn’t achievable.

We are at an exceptional point in the market when it comes to the possibilities of AI. Most organisations haven’t even begun to scratch the surface and there are plenty of low-hanging fruits that can be quickly and easily captured. To illustrate this, here are a few very simple yet powerful use cases that we have discussed in recent months with clients:

  • Churn predictor: using Machine Learning over existing internal data to predict which customers are most likely to leave and focus your retention efforts more accurately. If you can reduce your churn levels and maintain your underlying customer acquisition, your growth automatically increases! This doesn’t need setting up a data lake or buying social media feeds, you can generally get good results with internal data
  • Onboarding process automation: using a Robotic Process Automation tool, you can (very quickly) automate the (usually very manual) process of running any customer checks, setting up details on different systems etc. Over time you can apply Machine Learning to this so that any exceptions are learnt from and your manual involvement keeps going down
  • Sales team augmentation: there are multiple tools already in the market that can be implemented to augment your sales team’s capabilities. From virtual PAs that allow your team to hand off scheduling and meeting booking to a machine, to intelligent insight preparation based on your internal CRM and external news feeds that can warn your sales team when they should be contacting a customer or what products they are likely to be interested in
  • Agent Help desk: if you rely on an external network of agents to bring you business and be the face of your brand, you need to make sure they understand your products, tools and processes. Often queries are very simple and easy to deal with but require a human to interact with the agent, introducing delay and increasing the likelihood of confusion. A simple chatbot interface can help address the majority of queries quickly and inexpensively allowing the human team to focus on the more complex questions

To address these types of challenges better, we have set up DELTA AI as part of the Projecting Group. At DELTA AI we are already working with various clients on implementing AI solutions. DELTA AI doesn’t build software but bridges the knowledge, cultural and language gaps between our clients and startups to deliver practical solutions, simply and inexpensively. In the coming months we will be posting a series of articles detailing what types of solutions are feasible in each part of the financial services sector. In the meantime, for more information please visit the DELTA AI website (delta-ai.com) or speak to any of the Projecting team.

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