These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.
The eviction squads arrive in convoys, whisking attorneys, police officers, bank agents and teams of movers through the gates of the Cañadas del Florido housing development. Squatters ignore the signs and take over, their flickering candles casting an eerie glow at night. Maria De Jesus Silva's turn came on a spring day in 2014, when she found a thick stack of foreclosure documents on her doorstep. According to the documents, the Bank of New York Mellon, acting as trustee on behalf of bondholders, had started the long legal process to evict Silva from the two-bedroom home she bought in 2006 and shared with her daughter, son-in-law and granddaughter. "I was shocked because New York Mellon is a very powerful bank, and I'm a very poor person," said Silva, who makes $225 a month working at a gas station.
The hedge fund manager and former Goldman Sachs partner was addressing concerns about subprime mortgage loans by a bank he formerly ran when the name came up. "The most troubling loan was actually to the Octomom and we worked very, very hard... to move her to another home," Mnuchin said while addressing the practice of offering subprime loans. And here is the moment a Trump cabinet nominee referenced Octomom at his confirmation hearing. In a matter of moments, the moniker "Octomom" became a nationally trending topic on Twitter. The bank in question was OneWest, which Mnuchin formed to purchase what was left of subprime lender Indy Mac from the Federal Deposit Insurance Corporation in 2009 following the country's massive financial crisis, CNN reported last month.
You may not think the number of words in an email subject line says anything about you, but at least one company is betting that the metric can help determine your likelihood of paying back a loan. LenddoEFL, based in Singapore, is one of a handful of startups using alternative data points for credit scoring. Those companies review behavioral traits and smartphone habits to build models of creditworthiness for consumers in emerging markets, where standard credit reporting barely exists. In addition to analyzing financial-transaction data, Lenddo's algorithm takes into consideration things such as whether you avoid one-word subject lines (meaning you care about details) and regularly use financial apps on your smartphone (meaning you take your finances seriously). Lenddo also looks at the ratio of smartphone photos in your library that were taken with a front-facing camera, since selfies indicate youth, helping the company divide people into customer segments.
Average long-term U.S. mortgage rates slid this week to their lowest level since February 2015, luring prospective purchasers during the spring home-buying season. Mortgage buyer Freddie Mac says the average rate on a 30-year, fixed-rate mortgage fell to 3.59 percent from 3.71 percent last week. The benchmark rate was far below the 3.66 percent level it marked a year ago. The average rate on 15-year fixed-rate mortgages declined to 2.88 percent from 2.98 percent last week. A recent speech by Federal Reserve Chair Janet Yellen reaffirmed the Fed's plans to move slowly in raising the interest rates it controls.