Mitigating fraud is a top priority for banks. fraudsters are becoming more creative and technologically savvy—they’re also using advanced technologies like machine learning—and new schemes to defraud banks are evolving rapidly.
Challenge standing in long lines and unpleasant interaction with bank personnel among things they hate most about their banking experience. Approach The use of natural language processing (NLP), a set of technologies aimed at recognizing human language and speech, has propelled the evolution of chatbots to the level where they can perform an impressive range […]
Challenge In the insurance industry, calculating rates and adjusting benefits is largely characterized by manual processing. Incoming email inquiries are captured and routed manually. Approach Automate essential steps in the processing of the insurance company’s incoming inquiries so that they can be assigned to the correct process and team, more than 95% of the time. […]
verify and issue loans while reducing the average time taken by a financial institution to make a credit decision from 3-7 days, reduce the hassle of going through lengthy paperwork and visiting banks.
Mortgage applications are primarily processed manually in the service back office. The enormous efforts employees spent on manually reviewing submitted documentation and transposing relevant application data creates high costs. An outdated approach.
Insurance fraud has plagued the industry for several decades. According to estimates, fraud accounts for up to 10% of all claim expenditure in Europe, leading to substantial losses for both insurers and their customers, as the unwarranted costs translate into higher premiums.
Equipped with our solution, built on a behavioural model, Poland’s top debt collection company can improve the efficiency of its recovery process by 20%.
As a result of deep refactoring, our client–a Polish branch of an international bank–has improved its existing churn prediction model by more than 10%.