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Trading & Capital Markets

Dividend Intelligence API

Income investors plan around cash they expect to receive. Almost no platform tells them what that cash is likely to be, or how safe it is.

Investing apps
Wealth managers
Income platforms
Pension providers

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

Income investing is one of the largest retail segments and one of the worst served by software. A typical platform shows a trailing dividend yield — a backward-looking number that says nothing about whether the next payment will arrive, be cut, or be larger than the last.

What an income investor actually needs is a forward view: when payments are expected, how much, in which currency, and how likely the company is to sustain them. That requires combining declared dividends, payment history, earnings cover and cash generation into a forecast with an honest confidence attached.

Dividend sustainability is also one of the more tractable prediction problems in equities. Cuts are usually preceded by observable deterioration — falling cover, rising leverage, weakening cash conversion — over several quarters. The signal is there; it simply has to be assembled.

Who buys this

Investing apps, Wealth managers, Income platforms, Pension providers

Category: Trading & Capital Markets

What the API returns

What comes back

Forecast dividend amounts and payment dates by security
Ex-dividend, record and payment dates including declared and estimated
Dividend sustainability score with contributing factors
Payout ratio and earnings cover trends
Cut and suspension risk indicators
Historical dividend series adjusted for splits and special dividends
Portfolio-level expected income aggregation with currency handling
Withholding tax estimates by issuer domicile and holder residence

Endpoints

Method	Path	Returns
GET	/v1/securities/{id}/dividends	Declared and forecast dividends
GET	/v1/securities/{id}/sustainability	Sustainability score and factors
GET	/v1/calendar	Upcoming ex-dividend and payment dates
POST	/v1/portfolio/income	Expected income for a portfolio over a horizon
GET	/v1/alerts/cuts	Securities showing elevated cut risk
GET	/v1/securities/{id}/history	Adjusted historical dividend series

Request and response

illustrative

Request

{
  "positions": [
    { "identifier": "GB0007980591", "quantity": 1200 },
    { "identifier": "US4592001014", "quantity": 80 }
  ],
  "horizon_months": 12,
  "base_currency": "GBP",
  "holder_residence": "GB"
}

Response

{
  "horizon_months": 12,
  "base_currency": "GBP",
  "expected_income_gross": 2184.60,
  "expected_income_net": 1998.21,
  "confidence": "medium",
  "payments": [
    {
      "identifier": "GB0007980591",
      "ex_date": "2026-11-13",
      "pay_date": "2026-12-18",
      "amount_per_share": 0.2900,
      "status": "declared",
      "gross": 348.00
    },
    {
      "identifier": "US4592001014",
      "ex_date": "2027-02-09",
      "pay_date": "2027-03-10",
      "amount_per_share": 1.6800,
      "status": "forecast",
      "confidence": 0.82,
      "gross": 106.18,
      "withholding_pct": 15.0
    }
  ],
  "risk_flags": [
    { "identifier": "GB0007980591", "flag": "cover_below_1_2", "severity": "medium" }
  ]
}

Where the data comes from

Corporate action announcements	Declared dividends from official company and exchange announcements
Company filings	Earnings and cash flow statements, which drive the cover and sustainability analysis
Historical dividend records	Long payment histories, adjusted for splits and capital changes
Exchange calendars	Ex-dividend conventions differ by market and get this wrong easily
Withholding tax reference data	Treaty rates by issuer domicile and holder residence
FX rates	Forward and spot, for multi-currency income projection

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Payment forecasting from declared dividends where available, falling back to pattern-based estimation with explicit confidence
Sustainability scoring from cover, cash conversion, leverage and payout trajectory together
Cut-risk modelling trained on historical cuts, validated on held-out periods
Special dividend detection so one-off payments do not distort forward forecasts
Calendar modelling of each issuer's payment rhythm, including irregular payers
Currency handling that projects income in the holder's base currency with FX uncertainty stated
Confidence decay over the forecast horizon — a payment fourteen months out is far less certain than the next one
Backtesting of forecast accuracy against what was actually paid

What it changes for the business

Income planning

Expected-income projections turn a holdings list into something an income investor can plan around.

Early warning

Cut-risk flags give users months of notice rather than a surprise announcement.

Retention in a loyal segment

Income investors hold longer and switch less when the tooling fits their strategy.

Tax clarity

Withholding estimates remove a persistent source of confusion and support tickets.

How it fits into your product

How it embeds

REST endpoints for security-level and portfolio-level queries
Calendar components and income projection charts ready to embed
Webhooks on declarations, cut-risk changes and approaching ex-dates
Batch processing for income projections across your whole client base
Multi-currency output with configurable FX assumptions
Entitlement controls for forecast depth by account tier

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 12 weeks

Discovery and data review

Weeks 1–2

We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.

Architecture and prototype

Weeks 3–4

You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.

Production build

Weeks 5–10

Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.

Integration and handover

Weeks 11–12

We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

How accurate are the forecasts?

Declared dividends are facts and carry no forecast error. Estimated future payments carry real uncertainty, which the API expresses as a confidence value that decays with horizon. We report measured accuracy from backtesting rather than asserting a figure.

Do you predict dividend cuts?

We score cut risk rather than predict cuts. The model identifies deteriorating cover, cash conversion and leverage, which historically precede cuts. It is a risk indicator, and presenting it as a prediction would overstate it.

How are withholding taxes handled?

Treaty rates by issuer domicile and holder residence, returned as an estimate. It is not tax advice, individual circumstances vary, and we would make that explicit in the output your users see.

What about irregular payers?

Companies with no settled rhythm are modelled with wider confidence bounds, and where the pattern is genuinely unpredictable the API says so rather than inventing a schedule.

Can this cover funds and ETFs as well as shares?

Yes. Distribution history and declared distributions work the same way, though fund-level sustainability analysis depends on the underlying holdings being available.

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