Recherche avancée

Sur d’autres sites (5325)

• Open Banking Security 101 : Is open banking safe ?

  3 décembre 2024, par Daniel Crough — Banking and Financial Services

  Open banking is changing the financial industry. Statista reports that open banking transactions hit $57 billion worldwide in 2023 and will likely reach $330 billion by 2027. According to ACI, global real-time payment (RTP) transactions are expected to exceed $575 billion by 2028.

  Open banking is changing how banking works, but is it safe ? And what are the data privacy and security implications for global financial service providers ?

  This post explains the essentials of open banking security and addresses critical data protection and compliance questions. We’ll explore how a privacy-first approach to data analytics can help you meet regulatory requirements, build customer trust and ultimately thrive in the open banking market while offering innovative financial products.

   

  

  

  EU Financial Services Analytics Maturity Report

  Discover trends, strategies, and opportunities to balance compliance and competitiveness.

  Download the report now

  What is open banking ?

  Open banking is a system that connects banks, authorised third-party providers and technology, empowering customers to securely share their financial data with other companies. At the same time, it unlocks access to more innovative and personalised financial products and services like spend management solutions, tailored budgeting apps and more convenient payment gateways. 

  With open banking, consumers have greater choice and control over their financial data, ultimately fostering a more competitive financial industry, supporting technological innovation and paving the way for a more customer-centric financial future.

  Imagine offering your clients a service that analyses spending habits across all accounts — no matter the institution — and automatically finds ways to save them money. Envision providing personalised financial advice tailored to individual needs or enabling customers to apply for a mortgage with just a few taps on their phone. That’s the power of open banking.

  Embracing this technology is an opportunity for banks and fintech companies to build new solutions for customers who are eager for a more transparent and personalised digital experience.

  How is open banking different from traditional banking ?

  In traditional banking, consumers’ financial data is locked away and siloed within each bank’s systems, accessible only to the bank and the account holder. While account holders could manually aggregate and share this data, the process is cumbersome and prone to errors.

  With open banking, users can choose what data to share and with whom, allowing trusted third-party providers to access their financial information directly from the source. 

  

  How does open banking work ?

  The technology that makes open banking possible is the application programming interface (API). Think of banking APIs as digital translators for different software systems ; instead of translating languages, they translate data and code.

  The bank creates and publishes APIs that provide secure access to specific types of customer data, like credit card transaction history and account balances. The open banking API acts like a friendly librarian, ready to assist apps in accessing the information they need in a secure and organised way.

  Third-party providers, like fintech companies, use these APIs to build their applications and services. Some tech companies also act as intermediaries between fintechs and banks to simplify connections to multiple APIs simultaneously.

  For example, banks like BBVA (Spain) and Capital One (USA) offer secure API platforms. Fintechs like Plaid and TrueLayer use those banking APIs as a bridge to users’ financial data. This bridge gives other service providers like Venmo, Robinhood and Coinbase access to customer data, allowing them to offer new payment gateways and investment tools that traditional banks don’t provide.

  Is open banking safe for global financial services ?

  Yes, open banking is designed from the ground up to be safe for global financial services.

  Open banking doesn’t make customer financial data publicly available. Instead, it uses a secure, regulated framework for sharing information. This framework relies on strong security measures and regulatory oversight to protect user data and ensure responsible access by authorised third-party providers.

  In the following sections, we’ll explore the key security features and banking regulations that make this technology safe and reliable.

  Regulatory compliance in open banking

  Regulatory oversight is a cornerstone of open banking security.

  In the UK and the EU, strict regulations govern how companies access and use customer data. The revised Payment Services Directive (PSD2) in Europe mandates strong customer authentication and secure communication, promoting a high level of security for open banking services.

  To offer open banking services, companies must register with their respective regulatory bodies and comply with all applicable data protection laws.

  For example, third-party service providers in the UK must be authorised by the Financial Conduct Authority (FCA) and listed on the Financial Services Register. Depending on the service they provide, they must get an Account Information Service Provider (AISP) or a Payment Initiation Service Provider (PISP) license.

  Similar regulations and registries exist across Europe, enforced by the European National Competent Authority, like BaFin in Germany and the ACPR in France.

  In the United States, open banking providers don’t require a special federal license. However, this will soon change, as the U.S. Consumer Financial Protection Bureau (CFPB) unveiled a series of rules on 22 October 2024 to establish a regulatory framework for open banking.

  These regulations ensure that only trusted providers can participate in the open banking ecosystem. Anyone can check if a company is a trusted provider on public databases like the Regulated Providers registry on openbanking.org.uk. While being registered doesn’t guarantee fair play, it adds a layer of safety for consumers and banks.

  Key open banking security features that make it safe for global financial services

  Open banking is built on a foundation of solid security measures. Let’s explore five key features that make it safe and reliable for financial institutions and their customers.

  

  Strong Customer Authentication (SCA)

  Strong Customer Authentication (SCA) is a security principle that protects against unauthorised access to user financial data. It’s a regulated and legally required form of multi-factor authentication (MFA) within the European Economic Area.

  SCA mandates that users verify their identity using at least two of the following three factors :

  • Something they know (a password, PIN, security question, etc.)
  • Something they have (a mobile phone, a hardware token or a bank card)
  • Something they are (a fingerprint, facial recognition or voice recognition)

  This type of authentication helps reduce the risk of fraud and unauthorised transactions.

  API security

  PSD2 regulations mandate that banks provide open APIs, giving consumers the right to use any third-party service provider for their online banking services. According to McKinsey research, this has led to a surge in API adoption within the banking sector, with the largest banks allocating 14% of their IT budget to APIs. 

  To ensure API security, banks and financial service providers implement several measures, including :

  • API gateways, which act as a central point of control for all API traffic, enforcing security policies and preventing unauthorised access
  • API keys and tokens to authenticate and authorise API requests (the equivalent of a library card for apps)
  • Rate limiting to prevent denial-of-service attacks by limiting the number of requests a third-party application can make within a specific timeframe
  • Regular security audits and penetration testing to identify and address potential vulnerabilities in the API infrastructure

  Data minimisation and purpose limitation

  Data minimisation and purpose limitation are fundamental principles of data protection that contribute significantly to open banking safety.

  Data minimisation means third parties will collect and process only the data necessary to provide their service. Purpose limitation requires them to use the collected data only for its original purpose.

  For example, a budgeting app that helps users track their spending only needs access to transaction history and account balances. It doesn’t need access to the user’s full transaction details, investment portfolio or loan applications.

  Limiting the data collected from individual banks significantly reduces the risk of potential misuse or exposure in a data breach.

  Encryption

  Encryption is a security method that protects data in transit and at rest. It scrambles data into an unreadable format, making it useless to anyone without the decryption key.

  In open banking, encryption protects users’ data as it travels between the bank and the third-party provider’s systems via the API. It also protects data stored on the bank’s and the provider’s servers. Encryption ensures that even if a breach occurs, user data remains confidential.

  Explicit consent

  In open banking, before a third-party provider can access user data, it must first inform the user what data it will pull and why. The customer must then give their explicit consent to the third party collecting and processing that data.

  This transparency and control are essential for building trust and ensuring customers feel safe using third-party services.

  But beyond that, from the bank’s perspective, explicit customer consent is also vital for compliance with GDPR and other data protection regulations. It can also help limit the bank’s liability in case of a data breach.

  Explicit consent goes beyond sharing financial data. It’s also part of new data privacy regulations around tracking user behaviour online. This is where an ethical web analytics solution like Matomo can be invaluable. Matomo fully complies with some of the world’s strictest privacy regulations, like GDPR, lGPD and HIPAA. With Matomo, you get peace of mind knowing you can continue gathering valuable insights to improve your services and user experience while respecting user privacy and adhering to regulations.

  Risks of open banking for global financial services

  While open banking offers significant benefits, it’s crucial to acknowledge the associated risks. Understanding these risks allows financial institutions to implement safeguards and protect themselves and their customers.

  

  Risk of data breaches

  By its nature, open banking is like adding more doors and windows to your house. It’s convenient but also gives burglars more ways to break in.

  Open banking increases what cybersecurity professionals call the “attack surface,” or the number of potential points of vulnerability for hackers to steal financial data.

  Data breaches are a serious threat to banks and financial institutions. According to IBM’s 2024 Cost of a Data Breach Report, each breach costs companies in the US an average of $4.88 million. Therefore, banks and fintechs must prioritise strong security measures and data protection protocols to mitigate these risks.

  Risk of third-party access

  By definition, open banking involves granting third-party providers access to customer financial information. This introduces a level of risk outside the bank’s direct control.

  Financial institutions must carefully vet third-party providers, ensuring they meet stringent security standards and comply with all relevant data protection regulations.

  Risk of user account takeover

  Open banking can increase the risk of user account takeover if adequate security measures are not in place. For example, if a malicious third-party provider gains unauthorised access to a user’s bank login details, they could take control of the user’s account and make fraudulent bank transactions.

  A proactive approach to security, continuous monitoring and a commitment to evolving best practices and security protocols are crucial for navigating the open banking landscape.

  Open banking and data analytics : A balancing act for financial institutions

  The additional data exchanged through open banking unveils deeper insights into customer behaviour and preferences. This data can fuel innovation, enabling the development of personalised products and services and improved risk management strategies.

  However, using this data responsibly requires a careful balancing act.

  Too much reliance on data without proper safeguards can erode trust and invite regulatory issues. The opposite can stifle innovation and limit the technology’s potential.

  Matomo Analytics derisks web and app environments by giving full control over what data is tracked and how it is stored. The platform prioritises user data privacy and security while providing valuable data and analytics that will be familiar to anyone who has used Google Analytics.

  Open banking, data privacy and AI

  The future of open banking is entangled with emerging technologies like artificial intelligence (AI) and machine learning. These technologies significantly enhance open banking analytics, personalise services, and automate financial tasks.

  Several banks, credit unions and financial service providers are already exploring AI’s potential in open banking. For example, HSBC developed the AI-enabled FX Prompt in 2023 to improve forex trading. The bank processed 823 million client API calls, many of which were open banking.

  However, using AI in open banking raises important data privacy considerations. As the American Bar Association highlights, balancing personalisation with responsible AI use is crucial for open banking’s future. Financial institutions must ensure that AI-driven solutions are developed and implemented ethically, respecting customer privacy and data protection.

  Conclusion

  Open banking presents a significant opportunity for innovation and growth in the financial services industry. While it’s important to acknowledge the associated risks, security measures like explicit customer consent, encryption and regulatory frameworks make open banking a safe and reliable system for banks and their clients.

  Financial service providers must adopt a multifaceted approach to data privacy, implementing privacy-centred solutions across all aspects of their business, from open banking to online services and web analytics.

  By prioritising data privacy and security, financial institutions can build customer trust, unlock the full potential of open banking and thrive in today’s changing financial environment.

• Making Your First-Party Data Work for You and Your Customers

  11 mars, par Alex Carmona

  At last count, 162 countries had enacted data privacy policies of one kind or another. These laws or regulations, without exception, intend to eliminate the use of third-party data. That puts marketing under pressure because third-party data has been the foundation of online marketing efforts since the dawn of the Internet.

  Marketers need to future-proof their operations by switching to first-party data. This will require considerable adjustment to systems and processes, but the reward will be effective marketing campaigns that satisfy privacy compliance requirements and bring the business closer to its customers.

  To do that, you’ll need a coherent first-party data strategy. That’s what this article is all about. We’ll explain the different types of personal data and discuss how to use them in marketing without compromising or breaching data privacy regulations. We’ll also discuss how to build that strategy in your business. 

  So, let’s dive in.

  The different data types

  There are four distinct types of personal data used in marketing, each subject to different data privacy regulations.

  Before getting into the different types, it’s essential to understand that all four may comprise one or more of the following :

  Identifying data	Name, email address, phone number, etc.
  Behavioural data	Website activity, app usage, wishlist content, purchase history, etc.
  Transactional data	Orders, payments, subscription details, etc.
  Account data	Communication preferences, product interests, wish lists, etc.
  Demographic data	Age, gender, income level, education, etc.
  Geographic Data	Location-based information, such as zip codes or regional preferences.
  Psychographic Data	Interests, hobbies and lifestyle preferences.

  First-party data

  When businesses communicate directly with customers, any data they exchange is first-party. It doesn’t matter how the interaction occurs : on the telephone, a website, a chat session, or even in person.

  Of course, the parties involved aren’t necessarily individuals. They may be companies, but people within those businesses will probably share at least some of the data with colleagues. That’s fine, so long as the data : 

  • Remains confidential between the original two parties involved, and 
  • It is handled and stored following applicable data privacy regulations.

  The core characteristic of first-party data is that it’s collected directly from customer interactions. This makes it reliable, accurate and inherently compliant with privacy regulations — assuming the collecting party complies with data privacy laws.

  A great example of first-party data use is in banking. Data collected from customer interactions is used to provide personalised services, detect fraud, assess credit risk and improve customer retention.

  Zero-party data

  There’s also a subset of first-party data, sometimes called zero-party data. It’s what users intentionally and proactively share with a business. It can be preferences, intentions, personal information, survey responses, support tickets, etc.

  What makes it different is that the collection of this data depends heavily on the user’s trust. Transparency is a critical factor, too ; visitors expect to be informed about how you’ll use their data. Consumers also have the right to withdraw permission to use all or some of their information at any time.

  

  Second-party data

  This data is acquired from a separate organisation that collects it firsthand. Second-party data is someone else’s first-party data that’s later shared with or sold to other businesses. The key here is that whoever owns that data must give explicit consent and be informed of who businesses share their data with.

  A good example is the cooperation between hotel chains, car rental companies, and airlines. They share joint customers’ flight data, hotel reservations, and car rental bookings, much like travel agents did before the internet undermined that business model.

  Third-party data

  This type of data is the arch-enemy of lawmakers and regulators trying to protect the personal data of citizens and residents in their country. It’s information collected by entities that have no direct relationship with the individuals whose data it is.

  Third-party data is usually gathered, aggregated, and sold by data brokers or companies, often by using third-party cookies on popular websites. It’s an entire business model — these third-party brokers sell data for marketing, analytics, or research purposes. 

  Most of the time, third-party data subjects are unaware that their data has been gathered and sold. Hence the need for strong data privacy regulations.

  Benefits of a first-party data strategy

  First-party data is reliable, accurate, and ethically sourced. It’s an essential part of any modern digital marketing strategy.

  More personalised experiences

  The most important application of first-party data is customising and personalising customers’ interactions based on real behaviours and preferences. Personalised experiences aren’t restricted to websites and can extend to all customer communication.

  The result is company communications and marketing messages are far more relevant to customers. It allows businesses to engage more meaningfully with them, building trust and strengthening customer relationships. Inevitably, this also results in stronger customer loyalty and better customer retention.

  Greater understanding of customers

  Because first-party data is more accurate and reliable, it can be used to derive valuable insights into customer needs and wants. When all the disparate first-party data points are centralised and organised, it’s possible to uncover trends and patterns in customer behaviour that might not be apparent using other data.

  This helps businesses predict and respond to customer needs. It also allows marketing teams to be more deliberate when segmenting customers and prospects into like-minded groups. The data can also be used to create more precise personas for future campaigns or reveal how likely a customer would be to purchase in response to a campaign.

  Build trust with customers

  First-party data is unique to a business and originates from interactions with customers. It’s also data collected with consent and is “owned” by the company — if you can ever own someone else’s data. If treated like the precious resource, it can help businesses build trust with customers.

  However, developing that trust requires a transparent, step-by-step approach. This gradually strengthens relationships to the point where customers are more comfortable sharing the information they’re asked for.

  However, while building trust is a long and sometimes arduous process, it can be lost in an instant. That’s why first-party data must be protected like the Crown Jewels.

  

  Components of a first-party data strategy

  Security is essential to any first-party data strategy, and for good reason. As Gartner puts it, a business must find the optimal balance between business outcomes and data risk mitigation. Once security is baked in, attention can turn to the different aspects of the strategy.

  Data collection

  There are many ways to collect first-party data ethically, within the law and while complying with data privacy regulations, such as Europe’s General Data Protection Regulation (GDPR). Potential sources include :

  Website activity	forms and surveys, behavioural tracking, cookies, tracking pixels and chatbots
  Mobile app interactions	in-app analytics, push notifications and in-app forms
  Email marketing	newsletter sign-ups, email engagement tracking, promotions, polls and surveys
  Events	registrations, post-event surveys and virtual event analytics
  Social media interaction	polls and surveys, direct messages and social media analytics
  Previous transactions	purchase history, loyalty programmes and e-receipts
  Customer service	call centre data, live chat, chatbots and feedback forms
  In-person interactions	in-store purchases, customer feedback and Wi-Fi sign-ins
  Gated content	whitepapers, ebooks, podcasts, webinars and video downloads
  Interactive content	quizzes, assessments, calculators and free tools
  CRM platforms	customer profiles and sales data
  Consent management	privacy policies, consent forms, preference setting

  Consent management

  It may be the final item on the list above, but it’s also a key requirement of many data privacy laws and regulations. For example, the GDPR is very clear about consent : “Processing personal data is generally prohibited, unless it is expressly allowed by law, or the data subject has consented to the processing.”

  For that reason, your first-party data strategy must incorporate various transparent consent mechanisms, such as cookie banners and opt-in forms. Crucially, you must provide customers with a mechanism to manage their preferences and revoke that consent easily if they wish to.

  Data management

  Effective first-party data management, mainly its security and storage, is critical. Most data privacy regimes restrict the transfer of personal data to other jurisdictions and even prohibit it in some instances. Many even specify where residents’ data must be stored.

  Consider this cautionary tale : The single biggest fine levied for data privacy infringement so far was €1.2 billion. The Irish Data Protection Commission imposed a massive fine on Meta for transferring EU users’ data to the US without adequate data protection mechanisms.

  Data security is critical. If first-party data is compromised, it becomes third-party data, and any customer trust developed with the business will evaporate. To add insult to injury, data regulators could come knocking. That’s why the trend is to use encryption and anonymisation techniques alongside standard access controls.

  Once security is assured, the focus is on data management. Many businesses use a Customer Data Platform. This software gathers, combines and manages data from many sources to create a complete and central customer profile. Modern CRM systems can also do that job. AI tools could help find patterns and study them. But the most important thing is to keep databases clean and well-organised to make it easier to use and avoid data silos.

  Data activation

  Once first-party data has been collected and analysed, it needs to be activated, which means a business needs to use it for the intended purpose. This is the implementation phase where a well-constructed first-party strategy pays off. 

  The activation stage is where businesses use the intelligence they gather to :

  • Personalise website and app experiences
  • Adapt marketing campaigns
  • Improve conversion rates
  • Match stated preferences
  • Cater to observed behaviours
  • Customise recommendations based on purchase history
  • Create segmented email campaigns
  • Improve retargeting efforts
  • Develop more impactful content

  Measurement and optimisation

  Because first-party data is collected directly from customers or prospects, it’s far more relevant, reliable, and specific. Your analytics and campaign tracking will be more accurate. This gives you direct and actionable insights into your audience’s behaviour, empowering you to optimise your strategies and achieve better results.

  The same goes for your collection and activation efforts. An advanced web analytics platform like Matomo lets you identify key user behaviour and optimise your tracking. Heatmaps, marketing attribution tools, user behaviour analytics and custom reports allow you to segment audiences for better traction (and collect even more first-party data).

  

  How to build a first-party data strategy

  There are five important and sequential steps to building a first-party data strategy. But this isn’t a one-time process. It must be revisited regularly as operating and regulatory environments change. There are five steps : 

  1. Audit existing data

  Chances are that customers already freely provide a lot of first-party data in the normal course of business. The first step is to locate this data, and the easiest way to do that is by mapping the customer journey. This identifies all the touchpoints where first-party data might be found.

  2. Define objectives

  Then, it’s time to step back and figure out the goals of the first-party data strategy. Consider what you’re trying to achieve. For example :

  • Reduce churn 
  • Expand an existing loyalty programme
  • Unload excess inventory
  • Improve customer experiences

  Whatever the objectives are, they should be clear and measurable.

  3. Implement tools and technology

  The first two steps point to data gaps. Now, the focus turns to ethical web analytics with a tool like Matomo. 

  To further comply with data privacy regulations, it may also be appropriate to implement a Consent Management Platform (CMP) to help manage preferences and consent choices.

  4. Build trust with transparency

  With the tools in place, it’s time to engage customers. To build trust, keep them informed about how their data is used and remind them of their right to withdraw their consent. 

  Transparency is crucial in such engagement, as outlined in the 7 GDPR principles.

  5. Continuously improve

  Rinse and repeat. The one constant in business and life is change. As things change, they expose weaknesses or flaws in the logic behind systems and processes. That’s why a first-party data strategy needs to be continually reviewed, updated, and revised. It must adapt to changing trends, markets, regulations, etc. 

  Tools that can help

  Looking back at the different types of data, it’s clear that some are harder and more bothersome to get than others. But capturing behaviours and interactions can be easy — especially if you use tools that follow data privacy rules.

  But here’s a tip. Google Analytics 4 isn’t compliant by default, especially not with Europe’s GDPR. It may also struggle to comply with some of the newer data privacy regulations planned by different US states and other countries.

  Matomo Analytics is compliant with the GDPR and many other data privacy regulations worldwide. Because it’s open source, it can be integrated with any consent manager.

  Get started today by trying Matomo for free for 21 days,
  no credit card required.

  Try Matomo for free

• Multiple Dialogue lines of an ASS subtitle file is displayed at the same time on the video file

  14 janvier 2024, par Furkan Gözükara

  I am trying to code an ASS subtitle burner.

  


  Converting given SRT file into ASS subtitle

  


  Let me show examples

  


  Below is given SRT file - generated with Whisper

  


  1&#xA;00:00:00,000 --> 00:00:00,080&#xA;<u>American</u> XL Bully Dog&#xA;&#xA;2&#xA;00:00:00,080 --> 00:00:00,640&#xA;American <u>XL</u> Bully Dog&#xA;&#xA;3&#xA;00:00:00,640 --> 00:00:01,140&#xA;American XL <u>Bully</u> Dog&#xA;&#xA;4&#xA;00:00:01,140 --> 00:00:01,280&#xA;American XL Bully <u>Dog</u>&#xA;&#xA;5&#xA;00:00:01,280 --> 00:00:01,520&#xA;<u>is</u> a danger to&#xA;&#xA;6&#xA;00:00:01,520 --> 00:00:01,640&#xA;is <u>a</u> danger to&#xA;&#xA;7&#xA;00:00:01,640 --> 00:00:01,800&#xA;is a <u>danger</u> to&#xA;&#xA;8&#xA;00:00:01,800 --> 00:00:02,220&#xA;is a danger <u>to</u>&#xA;&#xA;9&#xA;00:00:02,220 --> 00:00:02,380&#xA;<u>our</u> communities, particularly our&#xA;&#xA;10&#xA;00:00:02,380 --> 00:00:02,680&#xA;our <u>communities,</u> particularly our&#xA;&#xA;11&#xA;00:00:02,680 --> 00:00:03,360&#xA;our communities, particularly our&#xA;&#xA;12&#xA;00:00:03,360 --> 00:00:03,580&#xA;our communities, <u>particularly</u> our&#xA;&#xA;13&#xA;00:00:03,580 --> 00:00:04,060&#xA;our communities, particularly <u>our</u>&#xA;&#xA;14&#xA;00:00:04,060 --> 00:00:04,280&#xA;<u>children.</u>&#xA;

  


  Then this above SRT file is converted into the below ASS subtitle

  


  [Script Info]
ScriptType: v4.00+
PlayResX: 384
PlayResY: 288

[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
Style: Default,Arial,16,&H00FFFFFF,&H0000FF00,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,0,2,10,10,10,1

[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
Dialogue: 0,00:00:00.000,00:00:00.080,Default,,0,0,0,,{\c&H00FF00&}American{\c&HFFFFFF&} XL Bully Dog
Dialogue: 0,00:00:00.080,00:00:00.640,Default,,0,0,0,,American {\c&H00FF00&}XL{\c&HFFFFFF&} Bully Dog
Dialogue: 0,00:00:00.640,00:00:01.140,Default,,0,0,0,,American XL {\c&H00FF00&}Bully{\c&HFFFFFF&} Dog
Dialogue: 0,00:00:01.140,00:00:01.280,Default,,0,0,0,,American XL Bully {\c&H00FF00&}Dog{\c&HFFFFFF&}
Dialogue: 0,00:00:01.280,00:00:01.520,Default,,0,0,0,,{\c&H00FF00&}is{\c&HFFFFFF&} a danger to
Dialogue: 0,00:00:01.520,00:00:01.640,Default,,0,0,0,,is {\c&H00FF00&}a{\c&HFFFFFF&} danger to
Dialogue: 0,00:00:01.640,00:00:01.800,Default,,0,0,0,,is a {\c&H00FF00&}danger{\c&HFFFFFF&} to
Dialogue: 0,00:00:01.800,00:00:02.220,Default,,0,0,0,,is a danger {\c&H00FF00&}to{\c&HFFFFFF&}
Dialogue: 0,00:00:02.220,00:00:02.380,Default,,0,0,0,,{\c&H00FF00&}our{\c&HFFFFFF&} communities, particularly our
Dialogue: 0,00:00:02.380,00:00:02.680,Default,,0,0,0,,our {\c&H00FF00&}communities,{\c&HFFFFFF&} particularly our
Dialogue: 0,00:00:02.680,00:00:03.360,Default,,0,0,0,,our communities, particularly our
Dialogue: 0,00:00:03.360,00:00:03.580,Default,,0,0,0,,our communities, {\c&H00FF00&}particularly{\c&HFFFFFF&} our
Dialogue: 0,00:00:03.580,00:00:04.060,Default,,0,0,0,,our communities, particularly {\c&H00FF00&}our{\c&HFFFFFF&}
Dialogue: 0,00:00:04.060,00:00:04.280,Default,,0,0,0,,{\c&H00FF00&}children.{\c&HFFFFFF&}


  


  Both when playing the subtitle in any video player or burning into video via FFMPEG, what happens is, multiple Dialogue lines are being displayed at the same time on the screen.

  


  I am doing a lot of research regarding this but still couldn't find out the issue.

  


  Here screenshot of what I mean. So how can I fix this issue ? What is wrong with my ASS file format ?

  


  

  


  Here below the functio that I am use to generate that ASS format

  


  def convert_srt_to_ass(srt_content):&#xA;    # ASS header&#xA;    ass_header = (&#xA;        "[Script Info]\n"&#xA;        "ScriptType: v4.00&#x2B;\n"&#xA;        "PlayResX: 384\n"&#xA;        "PlayResY: 288\n\n"&#xA;        "[V4&#x2B; Styles]\n"&#xA;        "Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding\n"&#xA;        "Style: Default,Arial,16,&amp;H00FFFFFF,&amp;H0000FF00,&amp;H00000000,&amp;H00000000,0,0,0,0,100,100,0,0,1,1,0,2,10,10,10,1\n\n"&#xA;        "[Events]\n"&#xA;        "Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text\n"&#xA;    )&#xA;&#xA;    ass_content = ass_header&#xA;    # Adjust regex to properly capture subtitle number, start time, end time, and text&#xA;    matches = list(re.finditer(r&#x27;(\d&#x2B;)\n(\d{2}:\d{2}:\d{2},\d{3}) --> (\d{2}:\d{2}:\d{2},\d{3})\n(.&#x2B;?)\n\n&#x27;, srt_content, re.DOTALL))&#xA;&#xA;    prev_end = None&#xA;    &#xA;    for match in matches:&#xA;        start, end, text = match.group(2), match.group(3), match.group(4)&#xA;        start = start.replace(&#x27;,&#x27;, &#x27;.&#x27;)&#xA;        end = end.replace(&#x27;,&#x27;, &#x27;.&#x27;)&#xA;&#xA;        # Calculate the correct start and end times to ensure no overlap&#xA;        if prev_end and start &lt;= prev_end:&#xA;            # Adjust the previous end time to be a bit before the current start time&#xA;            prev_end_time = datetime.strptime(prev_end, &#x27;%H:%M:%S.%f&#x27;)&#xA;            adjusted_end_time = prev_end_time - timedelta(milliseconds=100)  # Adjust by 100 milliseconds&#xA;            prev_end = adjusted_end_time.strftime(&#x27;%H:%M:%S.%f&#x27;)[:-3]  # Truncate to 3 decimal places&#xA;&#xA;            ass_content = ass_content.rstrip()&#xA;            ass_content = re.sub(r&#x27;(\d{2}:\d{2}:\d{2}\.\d{3}),Default,,$&#x27;, f&#x27;{prev_end},Default,,&#x27;, ass_content, 1)&#xA;            ass_content &#x2B;= &#x27;\n&#x27;&#xA;&#xA;        prev_end = end&#xA;&#xA;        # Formatting the text and adding it to the content&#xA;        text = text.replace(&#x27;<u>&#x27;, &#x27;{\\c&amp;H00FF00&amp;}&#x27;).replace(&#x27;</u>&#x27;, &#x27;{\\c&amp;HFFFFFF&amp;}&#x27;)&#xA;        text = text.replace(&#x27;\n&#x27;, &#x27;\\N&#x27;)  # Convert newlines within text for ASS format&#xA;        ass_content &#x2B;= f"Dialogue: 0,{start},{end},Default,,0,0,0,,{text}\n"&#xA;&#xA;        &#xA;        # Conversion of text and other formatting remains the same&#xA;&#xA;    return ass_content&#xA;