In many parts of the healthcare economy (e.g., in the US, Germany, India), the current process for hospital claims management is cumbersome and inefficient. Moreover, incorrect claims amounts that should not be paid but slip through the cracks in audit procedures constitute additional financial potential waiting to be unlocked. At present, health insurers could by industry expert estimates, reduce the total amount of money originally submitted in claims by about 3% which is worth billions of dollars globally. For example, an increase of 1% savings alone would give German health insurers additional savings of around EUR 500 million each– significant savings from which both the insurer and the insured community benefit. The AI-based claims management tools we are building are targeted towards high hit rates coupled with low effort via automation to process claims efficiently and effectively. Our machine learning algorithms enable reliable identification of claims that are incorrect based on AI-driven pattern recognition. Our products are being continually trained on thousands of claims from each market and aim to identify only those claims for the probability of successful intervention is high and, route majority of cases toward fully automated background processing so your administrative staff effectively focuses capacity on cases that require review. Our artificial intelligence-driven claims management suite can be used by insurers to identify correlations among unusual claims which help determine the likelihood of a successful intervention; the system learns with every new claim received.