The knowledge engine for finance

Project Information

Plural AI

Grant agreement ID: 882725

DOI

10.3030/882725

Project closed

EC signature date 3 December 2019

Start date 1 October 2019

End date 31 December 2019

Funded under

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Total cost

€ 71 429,00

EU contribution

€ 50 000,00

Coordinated by

PLURAL AI LIMITED
United Kingdom

Periodic Reporting for period 1 - Plural AI (The knowledge engine for finance)

Reporting period: 2019-10-01 to 2019-12-31

Summary of the context and overall objectives of the project

Investments boost economic growth but destroy value
Investment into companies is a fundamental driver of economic growth and competitiveness, helping to foster innovation and improve productivity, however research shows that 60% of M&A deals actually destroy value. The global M&A market is valued at ~€4 trillion, meaning ~€2.4 trillion is wasted. This is despite the average M&A deal taking 6-months, €500k-€1 million spent on commercial due diligence costs
Poor data quality and unscientific analysis in due diligence
This can be attributed to poor quality data and unscientific analysis in due diligence, with analysts unable to accurately assess market risks and opportunities, resulting in overestimated deal synergies and valuations. Financial analysis is hampered by the need to analyse and extract information from multiple, hard to parse, heterogeneous data sources (company websites, research reports, financial statements, social media, etc.), which requires human analysts to sift through thousands of documents and manually crunch numbers.
The process is time and cost intensive (a, and unable to scale given exponentially increasing amounts of web data. Existing solutions (offshore consultancies, financial data providers) are not scalable as they rely on humans and basic technology for data collection.
EU Impact - Fraud prevention/detecting systemic risk: The EC has a strict anti-fraud policy and has tasked OLAF with implementing it. Between 2010 and 2017, OLAF has concluded over 1,800 investigations, recommended the recovery of over €6.6 billion to the EU budget and issued over 2,300 recommendations for judicial, financial, disciplinary and administrative action to be taken by the competent authorities of the Member States and the EU. The Plural AI engine could contribute to a substantial reduction in this figure by detecting abnormalities and unusual activity.
We completed a technological roadmap, customer validation, pricing model assessment, risk assessment, IP assessment and updated our Business plan.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Technical roadmap: We assessed potential improvements to our existing platform and drafted an updated technical roadmap for Phase 2 implementation, covering the following aspects:
o ML predictions: we assessed 5 approaches for financial time-series predictions, and focused on improving the accuracy of our underlying dataset as a pre-requisite
o ML explainability: we assessed 6 approaches, implemented PoC models for 3 of them, and decided not to include it in Phase 2 based on user feedback
o ML infrastructure: we assessed 3 potential systems and found one outperformed the others
Customer validation: We interviewed 90 users for feedback and to make sure our proposed roadmap is in line with customer needs, and ran 3 experiments. We also looked into requirements for international expansion, and identified 11 countries of interest, with a short-list of
Pricing model: We reviewed 3 potential pricing plans, and eventually settled on one of them as the most optimal and best-understood by customers. We designed a revised pricing matrix.
Risk assessment: We conducted a thorough review of potential technical, commercial, and managerial/operational risks, and assessed each of them for likelihood and impact.
IP strategy: We reviewed our current IP assets, sought advice on our options for IP protection, and outlined our IP strategy going into Phase 2.
Business plan: We wrote a detailed business plan summarizing points previously discussed throughout the feasibility study.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Plural AI value beyond the state-of-the-art: Plural AI’s knowledge engine will be the first solution to offer an automated, scalable, data-driven platform to help finance professionals identify investment opportunities by instantly generating actionable insights on companies/markets from raw data. This moves beyond traditional search engines which index and retrieve third-party results (general-purpose, cannot answer complex financial questions), and data providers (limited scope, non-scalable).
EU Impacts - Boosts policy priorities: Member States and the European Commission work together to boost AI “made in Europe”. It wants to maximize investments through partnerships, create European data spaces, nurture talent, skills and life-long learning and develop ethical and trustworthy AI. Plural AI could be one of the European gamechangers in a sector that is active in a sector where with immense financial impact.
Environmental: Easier discovery and verification of ESG (Environmental/Social/Governance) compliant investments – an active requirement for many large investment firms.

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