[ DEBT_CAPITAL_MARKETS ]

Debt Market League Tables

2026Finance & Capital MarketsIn-depth

What it is

League-table analytics for the Indian debt market, by issuer, arranger, trustee and registrar.

Client work is shown without identifying imagery

[ THE_PROBLEM ]

Why this existed

Arrangers and trustees pitch on league-table position, but the underlying rankings are locked inside expensive terminals or produced manually once a quarter.

[ WHAT_WE_BUILT ]

What we built

Dedicated entity pages for every market participant type with detailed drill-downs, plus a purpose-built chart library — dual-axis volume-versus-count, sector distribution, credit-rating breakdown, monthly volume and count trends, redemption schedules, stacked and issuer-size comparisons. Sortable, paginated tables with user-selectable columns sit alongside them, under an advanced filtering system and authenticated access.

Dedicated entity pages for issuers, arrangers, trustees, registrars and rating agencies
Purpose-built chart set: dual-axis volume versus count, sector distribution, rating breakdown
Monthly volume and count trend visualisations
Redemption schedule and stacked issuer-size comparisons
Sortable, paginated tables with user-selectable columns
Advanced filtering across the dataset
Authenticated access with role handling

[ HOW_IT_IS_USED ]

How a company uses it

Arrangers and trustees use league-table position in pitches; issuers use it to pick counterparties. The same shape works for any market where "who did the most business, sliced how you like" is the product.

Built with

Next.jsReactRechartsFramer Motion

[ COMMON_QUESTIONS ]

Questions clients ask

Why so many bespoke chart types?

Because league-table analysis asks specific comparative questions — volume against deal count, distribution across sectors, concentration among top participants — and a generic bar chart answers none of them well. The charts are the product here, not decoration.

Where do the underlying numbers come from?

From a data pipeline feeding the platform, which is a separate build. That separation matters: the analytics layer and the ingestion layer have different failure modes and different maintenance rhythms.

Does this pattern work outside debt markets?

Yes. "Who did the most business, sliced how you like" is the shape of league tables in law, M&A, real estate and insurance broking alike. The entity model and the chart set are what transfer.

[ RELATED_WORK ]