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1mo

💡 An enlightening discussion on the critical issue of AI bias in financial decision-making. As we continue to integrate AI into our financial systems, it’s crucial to monitor and mitigate biases to ensure fairness and transparency. Donna Cichani , MBA shares insights from a study conducted VALID Systems ! #AIEthics #FinancialInclusion #TechForGood

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Valid. We have a product named Instant Funds and it helps. So if I give you a little bit of background about Instant Funds, it's a decision risk decisioning model for fraud and loss at the point of deposit. Each item gets assessed for risk of fraud, let's say. And in absence of risk, we pass that item and we give immediacy of access to funds to the consumers. And this is the normal practice is usually that. Item, let's say being a check deposited via mobile banking. It's pending for a certain number of hours up to few days. And as you can imagine, for small businesses or certain families who live paycheck to paycheck, that immediacy of access to funds is important. Because they're cash flow dependent. So we wanted to make sure that our model is not discriminating against certain groups, especially with false positives. So for one thing that I want to highlight is that this model guarantees the results. So it's already live at 7 of the top 20 FBI in if there is an instance that we pass an item and for immediacy of access to funds and that item happens to be. Fraudulent. We bear the risk and we eat the losses associated with it. So we monitored our results across two segments to through two angles. We looked at our data, one income level and the other one was minority level. So we divided the population into segments of high, minority, medium and low. And then same thing for income high, medium, low and below the poverty line and we. Check the performance across all of these segments that we defined using census data. Our pass rate was nearly identical across all segments. So that shows that we are not having, you're not having a higher false positive rate for certain groups who have let's say lower income. However, on the loss rate, which is the loss that we share with the with the financial institutions in case of us not detecting the risk was higher amongst low minority, low income and higher minority groups. Lower lower income and higher minority groups and that shows that if we were to be biased, we could introduce measures to reduce our loss as a company, and absence of such measures shows that we are. Within through an equitable lens, we are treating all of the segments. So that's a form of monitoring that we did internally. But the best practice it's always to hire an external impartial party to look into all of your models and detect any sign of bias.

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VALID Systems

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⚡ In today's enviornment where payment fraud is soaring, real time payment risk decisioning is is crucial. Our cutting-edge risk decisioning tools at VALID ensure you stay ahead of potential risks while optimizing your operations. With sub-second processing and proven accuracy, we’re here to keep your business secure and efficient. Ready to make decisions that matter in real-time? Let’s discuss how our solutions can safeguard your business 🔒 validadvantage.com #fintech #fraudprevention #financialsecurity #securetransactions #digitalbanking #aiinfinance #machinelearning
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Thank you for the kind words! We're happy to hear that our partnership has made such a positive impact. At VALID, we're committed to providing attentive and dependable service, and we're excited to continue helping you expand and improve your client experiences! validadvantage.com #fintech #fraudprevention #financialsecurity #securetransactions #digitalbanking #aiinfinance #machinelearning #riskmanagement #innovation #financialtechnology #cybersecurity #dataprotection #fintechsolutions #financialservices #techinfinance #futureoffinance #securebanking #blockchain #smartfinance #digitaltransformation
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