AC

Acin Financial Model

• Acin Terminal: web platform for managing, visualising, and benchmarking operational risk & controls data
• Acin Code / ACIN identifier: a unique "barcode" for each risk/control instance enabling cross-institution standardisation and network interoperability
• Network effect: risk intelligence shared across member institutions - a risk known to one is known to all; Acin acts as network hub
• Risk Intelligence reports: bespoke analysis (e.g. Covid-19 WFH, vendor incidents, rogue trading) produced from network data
• Value pillars: Data standardisation → benchmarking → network insights; reduces manual/Excel-based RCSA processes; provides regulator-ready quantitative evidence
• Predecessor: Anchura consulting (founded 2010, grew to £12m revenue by 2016); Acin spun out Sep 2017, pivoted fully to SaaS/data by Sep 2018

No explicit TAM/SAM/SOM figures in deck. Contextual size indicators (all):

• $150bn p.a. gross operational risk losses (ORX Banking Loss Report, 2018)
• $376bn in regulatory fines since 2012 (AFME, Apr 2018)
• $26bn annual regulatory compliance costs (AFME, Apr 2018)
• $411bn OpRisk capital held at top 30 banks

Addressable universe: global Tier 1/2 banks, plus interdealer brokers, exchanges, clearing houses, insurance. Market size figure for Acin's SaaS/data product not stated.

• Model described as "SaaS and Data Subscription Network"
• Revenue streams implied: (1) subscription fees per institution to access the Acin Terminal and network; (2) data subscription / benchmarking reports; (3) risk intelligence reports
• No per-seat, per-ACIN-code, or ACV figures shown
• Sales channel: direct enterprise sales to Tier 1 FIs (named clients suggest relationship-driven sales)

• Clients named (slide 3 timeline): Credit Suisse, Morgan Stanley, Nomura, Société Générale, JP Morgan, Bank of America Merrill Lynch, Standard Chartered, Deutsche Bank, UBS
• Unique Control Identifiers (ACIN codes) in dataset:
• Oct 2018: 1,200
• Jan 2019: 1,400
• Apr 2019: 2,200
• Jul 2019: 5,200
• Oct 2019: 5,900
• Jan 2020: 35,000
• Case study client ACIN counts: Bank 1 = 45 ACINs; Bank 2 = 163 ACINs; Bank 3 = 333 ACINs
• % control gaps vs. Acin standard: Bank 1 = 32%, Bank 2 = 55%, Bank 3 = 48%
• No ARR, MRR, revenue, or customer count figures disclosed

Not explicitly addressed in deck. Implied moats:

• Network effect: data value compounds with each additional institution
• Proprietary ACIN code taxonomy - "barcode for risk & controls" creates switching cost and standardisation lock-in
• Incumbent clients are the world's largest banks, creating credibility barrier for competitors
• Named regulatory references (FCA SM&CR, Basel II/III, ECB, ESMA) suggest deep domain expertise as a moat

• Cris Conde (Former President & CEO, SunGard) cited as endorser/advisor
• Founding team: not named in deck
• Prior revenue: Anchura consulting reached £12m revenue by 2016; no Acin-entity revenue or funding history shown

• Archetype + why: B2B SaaS ARR model with network-effect data layer. Acin sells annual subscriptions to financial institutions - recurring, contract-based, high-retention. The ACIN code dataset is the core asset; model should capture both the subscription revenue and the data/network expansion flywheel. A 3-statement is warranted given the institutional client profile and likely investor expectation.

• Forecast horizon & granularity: 5 years (FY2020–FY2024), quarterly for Y1–Y2, annual thereafter. Deck dated April 2020; company is early commercial stage.

• Key drivers & assumptions:
• Number of client institutions (FIs) - starting point: ~9 named clients as of April 2020; new client adds per year
• ACV per client
• Revenue per client type: differentiate by data subscription tier (number of ACINs licensed/benchmarked)
• Data/ACIN identifier growth rate - 1,200 → 35,000 unique codes Oct 2018–Jan 2020 (~29× in 15 months);
• Gross margin, moderate headcount for risk intelligence reports]
• S&M as % of revenue
• R&D as % of revenue
• G&A
• Churn / net revenue retention
• Payback period
• Risk intelligence report revenue

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: 4 new clients/yr, ACV £750k, 75% gross margin, 8% gross churn
• Bull: 7 new clients/yr, ACV £1.25m, faster data network growth driving premium pricing, 5% churn, expansion revenue from upsell
• Bear: 1–2 new clients/yr (long procurement cycles at Tier 1 banks), ACV £500k, higher S&M spend per logo, 12% churn

• Required sheets / outputs:

1. Assumptions (all drivers in one place)
2. Revenue build (clients × ACV, cohorted by year; ARR bridge: new + expansion − churn)
3. Income Statement (Revenue, COGS, Gross Profit, OpEx by function, EBITDA, D&A, EBIT)
4. Headcount plan (S&M, R&D, G&A, risk intelligence team)
5. Balance Sheet
6. Cash Flow Statement (incl. SaaS deferred revenue working capital)
7. KPI dashboard: ARR, clients, ACIN codes in network, NRR, CAC payback
8. Scenario toggle (Base / Bull / Bear)