Our first reading of the Saudi market, $295 Billion Hiding in Plain Sight, made a narrow, documented claim. Saudi banks under-earn on fee income by roughly twenty points against best-in-class peers. In a sector that size, every point is worth about SAR 1.5 billion of recurring, capital-light revenue a year, and closing the gap implies a market-cap unlock of up to $295 billion. The argument was arithmetic, not opinion.
This is the sequel. The same logic does not stop at the branch network. It generalises. The structural under-monetisation of advice, distribution, and data that shows up in retail banking shows up again in insurance and Takaful, and again across the capital markets that orbit the Saudi Exchange. The pools are different. The mechanism is identical.
Beyond Retail Banking
The banking gap was never really about banking. It was about a particular failure mode: institutions that hold the customer relationship, the transaction flow, and the underlying data, yet earn from balance-sheet activity rather than from the capital-light services that data makes possible. Spread income is taxed by capital. Fee income is not. The franchises that win the next decade are the ones that convert proprietary data and customer trust into recurring, advisory, and transactional fees.
Once you see the gap that way, it stops being a banking story. Any financial institution that sits on a rich, structured, proprietary dataset and monetises it thinly is carrying the same latent value. Saudi insurance carriers, Takaful operators, brokers, asset managers, and market-infrastructure providers all qualify. They each hold data the rest of the market would pay for, and they each leave most of that value on the table.
The banking gap was never about banking. It was about institutions that hold the data and earn from the balance sheet instead.
The Takaful and Insurance Opening
Saudi insurance and Takaful have spent a decade consolidating, recapitalising, and meeting a tightening supervisory bar. That work built scale. It did not, on its own, build fee-led economics. The sector still earns predominantly from underwriting and investment spread, and it under-monetises three things in particular.
The first is advice. A Takaful operator knows more about a policyholder's risk, life stage, and protection gap than almost any other counterparty, yet that knowledge rarely converts into governed, paid, recurring advisory relationships. The second is distribution. Carriers own the customer at the moment of greatest intent, renewal, claim, life event, and route most of that moment through low-margin, undifferentiated channels. The third is data. Claims histories, mortality and morbidity signals, motor telematics, and property exposure are among the most valuable structured datasets in the Kingdom, and they are largely consumed internally rather than productised into pricing services, risk analytics, or partner platforms.
None of this requires a regulatory leap or an exotic technology. It requires the same move the banking piece described: treat the proprietary dataset as the asset, and build governed AI delivery on top of it so that advice, distribution, and data each carry a price. The constraint is product architecture and data discipline, not capability.
Tadawul and the Data Moat
The capital markets that surround the Saudi Exchange tell the same story in a different register. As Tadawul has deepened, listings, derivatives, indices, post-trade infrastructure, the participants around it have grown into one of the region's richest sources of structured market data. Order flow, settlement records, index constituents, corporate-action histories, and investor analytics are precisely the inputs that global market operators have learned to monetise as their highest-margin business line.
In mature markets, the exchange and its ecosystem earn a growing share of revenue from data, analytics, and post-trade services rather than from transaction fees alone. The same pools exist around the Saudi Exchange. They are under-monetised today not because the data is thin, but because converting it into governed, productised, sellable analytics is a different discipline from running a matching engine. Brokers under-monetise client analytics. Asset managers under-monetise their research and risk data. Market infrastructure under-monetises reference and post-trade data.
The moat in every one of these cases is the same, and it is worth naming precisely. It is clean, structured, governed data, paired with AI delivery that a regulator, a board, and an auditor can all stand behind. Whoever assembles that first does not merely capture a fee pool. They set the reference price and the data standard the rest of the market then has to license. That is a durable moat, not a one-time gain.
The moat is clean structured data plus governed AI delivery. Whoever assembles it first sets the reference price the rest of the market has to license.
Sizing the Prize Honestly
Here the sequel has to be more disciplined than its predecessor, and we will say so plainly. In the banking piece, the number was defensible because the arithmetic was documented: a known fee-income gap, a known value per point, a known sector multiple. We could write $295 billion and show our work.
Insurance, Takaful, and the capital markets do not yet support that kind of single, clean headline number, and we will not manufacture one. The pools are real and, in our judgement, large. But sizing them credibly depends on inputs that are specific to each institution: the composition of its book, the quality and structure of its data, its distribution footprint, its regulatory posture, and the speed at which it can stand up governed AI delivery. A figure that ignores those inputs is decoration, not analysis.
So the honest answer is a method, not a headline. The fee-income logic generalises with high confidence. The precise prize for any one carrier, operator, broker, or market participant comes from an institution-specific model, the same kind we built for the banking case, run against that institution's own data. That model is available on request, and we would rather build it with you than guess at it in public.
The banking scoreboard told the sector what it was leaving on the table and proved it with arithmetic. The sequel makes a narrower, more durable point. The gap is not a banking anomaly. It is a market-wide pattern, the moat is data plus governed delivery, and the institution that builds it first in insurance, in Takaful, or on the exchange will define the category the others spend the decade catching.