The European Union’s new Retail Investment Strategy (RIS) will forever change how the quality of financial products is measured. It will no longer be enough for an investment to be merely “suitable”: it must demonstrate that it offers real Value for Money and that it acts in the best interest of the client.
But how can all this be measured—objectively and verifiably?
Raffaele Zenti, Co-Founder and COO of Wealthype and Adjunct Professor at the Politecnico di Milano, together with Ali Esmaeili Askari, Bizhan Zahedi, and Simone Pierro of Wealthype, have developed a proposal that places Artificial Intelligence at the center of this challenge: a transparent and explainable framework to translate the principles of RIS into numbers, indicators, and consistency maps connecting products, clients, and portfolios.
The MiFID II directive introduced the principles of suitability and appropriateness, requiring advisors to assess the client’s profile and offer consistent products.
The Retail Investment Strategy represents the next paradigm shift.
It’s no longer enough for a product to be formally suitable — it must also offer demonstrable value compared to similar solutions and contribute to the client’s overall financial well-being.
Value for Money (VfM): The ability to measure whether the costs and benefits of a product are balanced and justified compared to similar products.
Best Interest: The advisor must demonstrate that the proposed solution is not only “suitable” but truly in the client’s best interest, with documented evidence of its alignment with their needs and objectives.
In short, while regulatory compliance has so far relied on assumptions and qualitative interpretations, the RIS raises the bar.
Manufacturers and distributors must now prove with verifiable data that their products offer fair value for their cost and align with clients’ goals and needs.
European supervisory authorities — EIOPA for insurance products and ESMA for financial instruments and funds — are developing benchmarks and assessment methods based on statistical data and peer comparisons. However, these benchmarks will not be public.
Thus, a transparent and explainable architecture is needed to turn compliance from an act of trust into a process of proof and evidence.
The main challenge for financial operators is information asymmetry.
Authorities have access to aggregated data and statistical comparators, while manufacturers and distributors must demonstrate compliance without access to the same datasets.
Hence the need for tools capable of:
Translating regulatory obligations into measurable and replicable criteria;
Analyzing structured data and regulatory texts (e.g., KID PRIIPs, prospectuses, POG documents);
Providing clear and verifiable traces for every decision made.
The proposed solution is a transparent AI framework that makes complex concepts like Value for Money and Best Interest measurable through a unified, verifiable approach.
The model developed by Zenti, Askari, Zahedi, and Pierro combines structured data (costs, returns, risk indicators) with textual data (descriptions in KIDs or prospectuses), processed through Large Language Models (LLMs) and Explainable AI tools.
The result is a “digital twin” of the financial product: a numerical and semantic representation that enables the calculation of a Value for Money (VfM) score and verification of its alignment with client profiles.
At the portfolio level, everything converges into a single composite indicator: the Financial Wellness Index (FWI), which measures how well a combination of instruments meets the client’s goals and preferences.
In their paper, the authors tested the framework on three well-known funds:
H2O Multibonds, Carmignac Investissement, and iShares MSCI World ETF.
The system analyzed costs, returns, and the language of official documents.
Results:
iShares MSCI World ETF stands out for efficiency and transparency (VfM 0.80 out of 1);
Carmignac Investissement excels in consistency and governance;
H2O Multibonds shows weaknesses on both fronts.
The same methodology also evaluates compatibility with a typical client profile, showing that only products combining sound economic value and personal coherence are fully RIS-compliant.
The strength of the framework lies in its explainability: every step — from data extraction to score generation — is traceable and verifiable.
The algorithm doesn’t replace human judgment; it makes it verifiable, providing a clear explanation of why a product was assessed in a certain way.
In a world where transparency is power and explainability is survival, this approach could mark the turning point toward intelligent compliance — one that doesn’t fear regulators’ questions because it can show, step by step, how conclusions were reached.
The adoption of the RIS will not be a mere regulatory update but a cultural revolution — from formal compliance to demonstrable accountability.
As the researchers highlight, Artificial Intelligence, when applied with rigor, transparency, and human oversight, can make this transformation sustainable, credible, and measurable.
Or, in their own words:
“B ∩ C ⊆ A. That’s where compliance lives now.”
Translated: only where Best Interest and Value for Money meet does true compliance begin.
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