Posted by: data_guy_19 Joined: May 2023 Posts: 156 most of these AI underwriting and generative AI tools are only as good as the training data. and most carriers historical data is a disaster - inconsistent coding, missing fields, different systems that never talked. garbage in, garbage out. then everyone acts surprised when the risk scoring or fraud detection makes weird decisions. the demos always look perfect. real messy claims data is where it falls apart.

Posted by: old_timer_adjuster Joined: Feb 2013 Posts: 2,401 28 years in claims. every new tool is supposed to change everything. AI claims automation and fraud detection help with the easy stuff. the complicated claims still need experienced people. now we just spend extra time fighting the computer recommendations. customers can tell when an AI chatbot is talking to them and they get mad faster. some start stretching the truth because they think the system wont catch it. give me a sharp human adjuster over another “smart” model any day.

Posted by: Sarah_K Joined: Jan 2020 Posts: 613 @UW_Mike yeah the explainability problem is the same on our side. compliance and legal keep asking for audit trails and the vendor just says proprietary. @claims_lead_88 the generative AI inventing details is my biggest fear. one wrong sentence in a denial letter and were in trouble. anyone seeing carriers actually slow down or pause these generative AI and AI underwriting projects? or is everyone just pushing forward because the board wants AI in every slide deck?

Posted by: fraud_analyst_22 Joined: Nov 2019 Posts: 534 fraud detection side here. the newer AI models using graph analysis and behavioural signals are better than the old rules engines. we catch more staged accidents and provider billing patterns. but false positives are still killing us. simple auto insurance claims get held for review because the repair shop or the claimant “looks similar” to previous fraud cases. then the customer complains and we look bad. management only looks at the “fraud dollars prevented” number and ignores the customer experience cost.

Posted by: customer_exp_gal Joined: Jul 2022 Posts: 278 can we talk about the AI chatbots for a second? ours is supposed to handle FNOL and basic policy questions. customers hate it. they type “i need to file a claim” and it asks the same three questions over and over or dumps them into a form. half of them just hang up and call. conversational AI is still not ready for regulated insurance. people want a human when something goes wrong with their claim.

Posted by: UW_Mike Joined: Mar 2017 Posts: 890 AI underwriting is where the real pressure is. the predictive models and risk scoring tools are decent for high volume personal lines (car insurance and home insurance) but commercial is still a mess. biggest issue is the black box. agents and brokers want to know why a quote came back high and all we can say is “the model identified elevated risk factors”. not good enough when the client pushes back. we also tried generative AI for policy wording reviews. it hallucinated coverage that wasnt there twice. we shut that pilot down fast.

Posted by: claims_lead_88 Joined: Sep 2018 Posts: 1,043 weve been on AI claims automation for about 18 months. photo estimation on auto damage is solid now, especially for the easy fender benders. saves a ton of time. fraud detection is mixed. it catches some organised rings that humans would miss but it also flags way too many legitimate home insurance water claims. adjusters are burned out overriding the system. the generative AI part for writing claims summaries is hit or miss. sometimes it sounds professional, other times it invents details that arent in the file. that scares me for compliance.

Posted by: Sarah_K Joined: Jan 2020 Posts: 612 ok so our carrier just announced were rolling out generative AI for underwriting and claims notes. management is hyped about faster risk scoring and automated decisioning. anyone else dealing with this right now? our underwriters are already complaining the AI underwriting recommendations keep changing when the same application is run twice. and the explainability is still basically “trust the model”. also hearing a lot about AI claims automation and fraud detection getting better but i still see way too many false positives on simple auto insurance claims. whats the real experience out there? no vendor talk please.

Posted by: data_nerd_42 Joined: Apr 2022 Posts: 167 the models are only as good as the training data and most carriers have terrible historical data. years of inconsistent coding, missing fields, different systems that never talked to each other. then they feed that mess into a model and act surprised when it makes weird decisions. also most of these "AI" solutions are just vendor demos that look impressive until you put them on real messy claims data. then reality hits.

Posted by: old_school_adjuster Joined: Jan 2014 Posts: 2,156 been doing this 28 years. every few years they come out with some new system thats supposed to replace half of us. AI is just the latest. it helps with the easy stuff and makes the hard stuff harder because now you have to fight the computer on top of fighting the claim. customers can smell when theyre talking to a bot and they hate it. some of them start lying more because they figure the computer wont catch it. give me a good human adjuster with experience any day.

Posted by: Mike_R Joined: Mar 2019 Posts: 848 @claims_gal_23 yeah the DOI thing is what keeps me up at night. we already had one close call. @underwriter_ed black box is the biggest problem. compliance keeps asking for explainability and the vendor just says "proprietary model". anyone know of carriers that actually pulled the plug on these systems after trying them? or is everyone just quietly living with the problems because the board loves the AI slides.

Posted by: progressive_rep Joined: Aug 2018 Posts: 421 we use it pretty heavily and its mixed. the photo estimation tools for auto damage are legitimately good now. saves a ton of time on simple claims. fraud detection has gotten better too but yeah the false positives are still annoying. you learn which ones to ignore after a while. biggest issue is the people side. older adjusters hate it and refuse to trust anything the system says. younger ones over-rely on it and miss obvious things. training is a nightmare.

Posted by: tech_skeptic Joined: Feb 2020 Posts: 589 honestly most of what i've seen is just fancy rules engines with better marketing. true generative AI for things like policy language or complex liability analysis still feels years away from being reliable enough for regulated industries. one wrong denial letter generated by AI and you're looking at a class action or regulatory fine. the chatbots are the worst part. they make the company look incompetent. i'd rather just talk to a person who at least knows when they dont know something.

Posted by: underwriter_ed Joined: Jun 2016 Posts: 1,204 we're using it more on the underwriting side. the predictive models for risk scoring are actually pretty useful, especially for new business. catches some patterns humans miss. but the black box problem is real. when an agent asks why a quote came back so high the system just says "risk factors identified" and thats it. underwriters end up having to reverse engineer it. also the data quality is garbage half the time. garbage in garbage out. if the AI is trained on incomplete application data its just amplifying the mess.

Posted by: claims_gal_23 Joined: Nov 2021 Posts: 312 ugh same. we rolled out something similar last year for auto claims. the AI is decent at the simple rear-end stuff where liability is obvious but anything with multiple parties or injuries and it falls apart. our fraud model is constantly false positive-ing. had a guy with a legit theft claim get held up for 3 weeks because the AI thought his repair shop history looked "anomalous". he ended up complaining to the DOI. management still calls it a success though because the numbers look good on the dashboard if you ignore the escalations.

Posted by: Mike_R Joined: Mar 2019 Posts: 847 so my company (mid size auto/home) just started pushing this "AI claims automation" thing hard. management keeps saying its gonna cut processing time by 40% and reduce fraud. anyone else dealing with this? we got a pilot running on property claims and so far the system keeps flagging legitimate water damage as "suspicious patterns" because the photos look similar to other claims in the database. adjusters are spending more time overriding the AI than they used to just handling the claim. also the customer facing chatbot is trash. people are getting pissed when it cant answer basic questions about their policy and just loops them back to "i can help with that! please describe your issue" curious what other companies are seeing. real talk only please, not the marketing bullshit.

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User: Tobias_FrankfurtRides Posted: 7 hours ago Ähnliche Erfahrung hier. Ich finde den Konzept-Ansatz richtig stark – moderne Metro als Portal in eine historical journey. Besonders der Kontrast zwischen neon-beleuchteter Bahn und antiker Welt funktioniert visuell. Probleme: Bewegung der Menschen und Tiere aus ancient times wirkt oft noch steif. Auch kleine Details wie Schuhe oder Werkzeuge stimmen nicht immer. Aktuell nutze ich Hybrid: AI für die großen Einstellungen, dann leichte Nachbearbeitung für Realismus. Für kurze Reels schon brauchbar.

ser: Sophie_KölnEditor Posted: 11 hours ago Habe gestern Abend nochmal getestet. Fokus lag auf ancient times in Ägypten-Style. Der Moment, wenn die Bahn anhält und die Tür aufgeht, ist schon stark. Danach wird es schwieriger – Sand, Architektur und Kleidung müssen zusammenpassen. Was gut funktioniert hat: Sehr klare Prompts mit „photorealistic, historically accurate clothing, natural daylight, cinematic camera“. Trotzdem braucht man noch manuelles Nachschärfen. Komplett fertig aus der AI kommt es noch nicht.