Fund Financial Compliance Procedures - Germany

Utilizing data from German Commercial Code, Investment Code, Federal Financial Supervisory Authority (Bafin), Expert Commentary and Best-Practise Cases

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Data Amount

1 TB

Data Freshness

Updated July 2023

Use Cases




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Example output

What do I need to consider when it comes to the investor protection strengthening in Germany?

An Investor Protection Strengthening Law in Germany ("Gesetz zur weiteren Stärkung des Anlegerschutzes") as of 2023 will introduce changes for registered AIF capital management companies. The previous registration ability for such capital management companies is now void. This applies to those who had registration in accordance to Section 2, Paragraph 4 or 5 of the Capital Investment Code (KAGB). It also includes other significant changes for those AIFs with registration under Section 2, Paragraph 4 of KAGB. They are now subject to a comprehensive commercial accounting and auditing obligation (Section 45 KAGB). There will also be a new obligation to prepare a complete annual financial statement. AIF capital management companies must complete an audit within 9 months after the end of the fiscal year. This audit not only checks for compliance with KAGB provisions, but also with the regulations from the Money Laundering Act. Additionally, rules are expected to be issued for the provision of loans. There is a mandatory audit for companies starting their financial years after Dec 31, 2020, thus those with calendar year accounts must comply by Dec 31, 2021.

1. Bundesfinanzmisterium: "2021-07-16-Anlegerschutzstaerkungsgesetz"
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2. PwC Commentary: "AIFs subject to registration will be subject to audit"
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3. KPMG Commentary: "Changes for AIFs triggered by Protection Strengthening Law in Germany"
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Fund Financial Compliance Procedures - Germany

How is the financial data indexed and embeddings created?

How do we keep the data updated?

Our system is integrated with major legal text and blog crawler streams. Every week, a process runs to check for new data or updates to existing data. New and updated data is preprocessed, indexed, and embeddings are generated before it’s stored in our vector database. This ensures our AI embeddings always have the most recent financial market data.

How to use

Python Code

You can access the embedding using EmbedElite python package or curl request.

# this returns a list of embeddings
from embedelite import load_embedding

embeddings = load_embedding("fund-financial-compliance-procedures-germany")

# this returns an object which can be directly inserted into Qdrant
result = load_embedding("fund-financial-compliance-procedures-germany", embed_for="qdrant")
# result is {"embeddings": [], "documents": [] "ids": []}



curl -X POST -H "Content-Type: application/json" -d '{
"doc_id": "fund-financial-compliance-procedures-germany"


    "mappings": {
        "properties": {
        "doc_source": {"type": "keyword"},
        "sentence": { "type": "text" },
        "embeddings": { "type": "dense_vector", "dims": 1536, "index": False },
        "doc_source": {"type": "keyword"}